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1502.02072
3
# 1. Introduction Discovering new treatments for human diseases is an im- mensely complicated challenge. Prospective drugs must attack the source of an illness, but must do so while sat- isfying restrictive metabolic and toxicity constraints. Tra- ditionally, drug discovery is an extended process that takes years to move from start to finish, with high rates of failure along the way. The overall complexity of the virtual screening problem has limited the impact of machine learning in drug dis- covery. To achieve greater predictive power, learning al- gorithms must combine disparate sources of experimental data across multiple targets. Deep learning provides a flex- ible paradigm for synthesizing large amounts of data into predictive models. In particular, multitask networks facil- itate information sharing across different experiments and compensate for the limited data associated with any partic- ular experiment. Preliminary work. Under review by the International Conference on Machine Learning (ICML). Copyright 2015 by the author(s). In this work, we investigate several aspects of the multi- task learning paradigm as applied to virtual screening. We gather a large collection of datasets containing nearly 40 Massively Multitask Networks for Drug Discovery
1502.02072#3
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 3, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 1. Introduction\nDiscovering new treatments for human diseases is an im- mensely complicated challenge. Prospective drugs must attack the source of an illness, but must do so while sat- isfying restrictive metabolic and toxicity constraints. Tra- ditionally, drug discovery is an extended process that takes years to move from start to finish, with high rates of failure along the way.\nThe overall complexity of the virtual screening problem has limited the impact of machine learning in drug dis- covery. To achieve greater predictive power, learning al- gorithms must combine disparate sources of experimental data across multiple targets. Deep learning provides a flex- ible paradigm for synthesizing large amounts of data into predictive models. In particular, multitask networks facil- itate information sharing across different experiments and compensate for the limited data associated with any partic- ular experiment.\nPreliminary work. Under review by the International Conference on Machine Learning (ICML). Copyright 2015 by the author(s).\nIn this work, we investigate several aspects of the multi- task learning paradigm as applied to virtual screening. We gather a large collection of datasets containing nearly 40\nMassively Multitask Networks for Drug Discovery", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
4
In this work, we investigate several aspects of the multi- task learning paradigm as applied to virtual screening. We gather a large collection of datasets containing nearly 40 Massively Multitask Networks for Drug Discovery million experimental measurements for over 200 targets. We demonstrate that multitask networks trained on this col- lection achieve significant improvements over baseline ma- chine learning methods. We show that adding more tasks and more data yields better performance. This effect di- minishes as more data and tasks are added, but does not appear to plateau within our collection. Interestingly, we find that the total amount of data and the total number of tasks both have significant roles in this improvement. Fur- thermore, the features extracted by the multitask networks demonstrate some transferability to tasks not contained in the training set. Finally, we find that the presence of shared active compounds is moderately correlated with multitask improvement, but the biological class of the target is not.
1502.02072#4
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 4, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "In this work, we investigate several aspects of the multi- task learning paradigm as applied to virtual screening. We gather a large collection of datasets containing nearly 40\nMassively Multitask Networks for Drug Discovery\nmillion experimental measurements for over 200 targets. We demonstrate that multitask networks trained on this col- lection achieve significant improvements over baseline ma- chine learning methods. We show that adding more tasks and more data yields better performance. This effect di- minishes as more data and tasks are added, but does not appear to plateau within our collection. Interestingly, we find that the total amount of data and the total number of tasks both have significant roles in this improvement. Fur- thermore, the features extracted by the multitask networks demonstrate some transferability to tasks not contained in the training set. Finally, we find that the presence of shared active compounds is moderately correlated with multitask improvement, but the biological class of the target is not.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
5
While we were preparing this work, a workshop paper was released that also used massively multitask networks for virtual screening (Unterthiner et al.). That work curated a dataset of 1,280 biological targets with 2 million asso- ciated data points and trained a multitask network. Their network has more tasks than ours (1,280 vs. 259) but far fewer data points (2 million vs. nearly 40 million). The emphasis of our work is considerably different; while their report highlights the performance gains due to multitask networks, ours is focused on disentangling the underly- ing causes of these improvements. Another closely related work proposed the use of collaborative filtering for vir- tual screening and employed both multitask networks and kernel-based methods (Erhan et al., 2006). Their multitask networks, however, did not consistently outperform single- task models. # 2. Related Works
1502.02072#5
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 5, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "While we were preparing this work, a workshop paper was released that also used massively multitask networks for virtual screening (Unterthiner et al.). That work curated a dataset of 1,280 biological targets with 2 million asso- ciated data points and trained a multitask network. Their network has more tasks than ours (1,280 vs. 259) but far fewer data points (2 million vs. nearly 40 million). The emphasis of our work is considerably different; while their report highlights the performance gains due to multitask networks, ours is focused on disentangling the underly- ing causes of these improvements. Another closely related work proposed the use of collaborative filtering for vir- tual screening and employed both multitask networks and kernel-based methods (Erhan et al., 2006). Their multitask networks, however, did not consistently outperform single- task models.\n# 2. Related Works", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
6
# 2. Related Works Machine learning has a rich history in drug discovery. Early work combined creative featurizations of molecules with off-the-shelf learning algorithms to predict drug ac- tivity (Varnek & Baskin, 2012). The state of the art has moved to more refined models, such as the influence rele- vance voting method that combines low-complexity neural networks and k-nearest neighbors (Swamidass et al., 2009), and Bayesian belief networks that repurpose textual infor- mation retrieval methods for virtual screening (Abdo et al., 2010). Other related work uses deep recursive neural net- works to predict aqueous solubility by extracting features from the connectivity graphs of small molecules (Lusci et al., 2013). Within the greater context of deep learning, we draw upon various strands of recent thought. Prior work has used multitask deep networks in the contexts of language understanding (Collobert & Weston, 2008) and multi- language speech recognition (Deng et al., 2013). Our best-performing networks draw upon design patterns intro- duced by GoogLeNet (Szegedy et al., 2014), the winner of ILSVRC 2014. # 3. Methods # 3.1. Dataset Construction and Design
1502.02072#6
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 6, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 2. Related Works\nMachine learning has a rich history in drug discovery. Early work combined creative featurizations of molecules with off-the-shelf learning algorithms to predict drug ac- tivity (Varnek & Baskin, 2012). The state of the art has moved to more refined models, such as the influence rele- vance voting method that combines low-complexity neural networks and k-nearest neighbors (Swamidass et al., 2009), and Bayesian belief networks that repurpose textual infor- mation retrieval methods for virtual screening (Abdo et al., 2010). Other related work uses deep recursive neural net- works to predict aqueous solubility by extracting features from the connectivity graphs of small molecules (Lusci et al., 2013).\nWithin the greater context of deep learning, we draw upon various strands of recent thought. Prior work has used multitask deep networks in the contexts of language understanding (Collobert & Weston, 2008) and multi- language speech recognition (Deng et al., 2013). Our best-performing networks draw upon design patterns intro- duced by GoogLeNet (Szegedy et al., 2014), the winner of ILSVRC 2014.\n# 3. Methods\n# 3.1. Dataset Construction and Design", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
7
# 3. Methods # 3.1. Dataset Construction and Design Deep learning has made inroads into drug discovery in recent years, most notably in 2012 with the Merck Kag- gle competition (Dahl, November 1, 2012). Teams were given pre-computed molecular descriptors for compounds with experimentally measured activity against 15 targets and were asked to predict the activity of molecules in a held-out test set. The winning team used ensemble models including multitask deep neural networks, Gaussian pro- cess regression, and dropout to improve the baseline test set R2 by nearly 17%. The winners of this contest later released a technical report that discusses the use of mul- titask networks for virtual screening (Dahl et al., 2014). Additional work at Merck analyzed the choice of hyper- parameters when training single- and multitask networks and showed improvement over random forest models (Ma et al., 2015). The Merck Kaggle result has been received with skepticism by some in the cheminformatics and drug discovery communities (Lowe, December 11, 2012, and as- sociated comments). Two major concerns raised were that the sample size was too small (a good result across 15 sys- tems may well have occurred by chance) and that any gains in predictive accuracy were too small to justify the increase in complexity.
1502.02072#7
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 7, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 3. Methods\n# 3.1. Dataset Construction and Design\nDeep learning has made inroads into drug discovery in recent years, most notably in 2012 with the Merck Kag- gle competition (Dahl, November 1, 2012). Teams were given pre-computed molecular descriptors for compounds with experimentally measured activity against 15 targets and were asked to predict the activity of molecules in a held-out test set. The winning team used ensemble models including multitask deep neural networks, Gaussian pro- cess regression, and dropout to improve the baseline test set R2 by nearly 17%. The winners of this contest later released a technical report that discusses the use of mul- titask networks for virtual screening (Dahl et al., 2014). Additional work at Merck analyzed the choice of hyper- parameters when training single- and multitask networks and showed improvement over random forest models (Ma et al., 2015). The Merck Kaggle result has been received with skepticism by some in the cheminformatics and drug discovery communities (Lowe, December 11, 2012, and as- sociated comments). Two major concerns raised were that the sample size was too small (a good result across 15 sys- tems may well have occurred by chance) and that any gains in predictive accuracy were too small to justify the increase in complexity.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
8
Models were trained on 259 datasets gathered from pub- licly available data. These datasets were divided into four groups: PCBA, MUV, DUD-E, and Tox21. The PCBA group contained 128 experiments in the PubChem BioAssay database (Wang et al., 2012). The MUV group contained 17 challenging datasets specifically designed to avoid common pitfalls in virtual screening (Rohrer & Bau- mann, 2009). The DUD-E group contained 102 datasets that were designed for the evaluation of methods to pre- dict interactions between proteins and small molecules (Mysinger et al., 2012). The Tox21 datasets were used in the recent Tox21 Data Challenge (https://tripod. nih.gov/tox21/challenge/) and contained exper- imental data for 12 targets relevant to drug toxicity predic- tion. We used only the training data from this challenge because the test set had not been released when we con- structed our collection. In total, our 259 datasets contained 37.8M experimental data points for 1.6M compounds. De- tails for the dataset groups are given in Table 1. See the Appendix for details on individual datasets and their bio- logical target categorization.
1502.02072#8
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 8, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Models were trained on 259 datasets gathered from pub- licly available data. These datasets were divided into four groups: PCBA, MUV, DUD-E, and Tox21. The PCBA group contained 128 experiments in the PubChem BioAssay database (Wang et al., 2012). The MUV group contained 17 challenging datasets specifically designed to avoid common pitfalls in virtual screening (Rohrer & Bau- mann, 2009). The DUD-E group contained 102 datasets that were designed for the evaluation of methods to pre- dict interactions between proteins and small molecules (Mysinger et al., 2012). The Tox21 datasets were used in the recent Tox21 Data Challenge (https://tripod. nih.gov/tox21/challenge/) and contained exper- imental data for 12 targets relevant to drug toxicity predic- tion. We used only the training data from this challenge because the test set had not been released when we con- structed our collection. In total, our 259 datasets contained 37.8M experimental data points for 1.6M compounds. De- tails for the dataset groups are given in Table 1. See the Appendix for details on individual datasets and their bio- logical target categorization.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
9
It should be noted that we did not perform any prepro- cessing of our datasets, such as removing potential ex- perimental artifacts. Such artifacts may be due by comMassively Multitask Networks for Drug Discovery Table 1. Details for dataset groups. Values for the number of data points per dataset and the percentage of active compounds are reported as means, with standard deviations in parenthesis. Group Datasets Data Points / ea. % Active PCBA DUD-E MUV Tox21 128 102 17 12 282K (122K) 14K (11K) 15K (1) 6K (500) 1.8 (3.8) 1.6 (0.2) 0.2 (0) 7.8 (4.7)
1502.02072#9
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 9, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "It should be noted that we did not perform any prepro- cessing of our datasets, such as removing potential ex- perimental artifacts. Such artifacts may be due by comMassively Multitask Networks for Drug Discovery\nTable 1. Details for dataset groups. Values for the number of data points per dataset and the percentage of active compounds are reported as means, with standard deviations in parenthesis.\nGroup Datasets Data Points / ea. % Active PCBA DUD-E MUV Tox21 128 102 17 12 282K (122K) 14K (11K) 15K (1) 6K (500) 1.8 (3.8) 1.6 (0.2) 0.2 (0) 7.8 (4.7)", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
10
community (Jain & Nicholls, 2008), we used metrics de- rived from the receiver operating characteristic (ROC) curve to evaluate model performance. Recall that the ROC curve for a binary classifier is the plot of true positive rate (TPR) vs. false positive rate (FPR) as the discrimination threshold is varied. For individual datasets, we are inter- ested in the area under the ROC curve (AUC), which is a global measure of classification performance (note that AUC must lie in the range [0, 1]). More generally, for a collection of N datasets, we consider the mean and median K-fold-average AUC: pounds whose physical properties cause interference with experimental measurements or allow for promiscuous in- teractions with many targets. A notable exception is the MUV group, which has been processed with consideration of these pathologies (Rohrer & Baumann, 2009). # 3.2. Small Molecule Featurization
1502.02072#10
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 10, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "community (Jain & Nicholls, 2008), we used metrics de- rived from the receiver operating characteristic (ROC) curve to evaluate model performance. Recall that the ROC curve for a binary classifier is the plot of true positive rate (TPR) vs. false positive rate (FPR) as the discrimination threshold is varied. For individual datasets, we are inter- ested in the area under the ROC curve (AUC), which is a global measure of classification performance (note that AUC must lie in the range [0, 1]). More generally, for a collection of N datasets, we consider the mean and median K-fold-average AUC:\npounds whose physical properties cause interference with experimental measurements or allow for promiscuous in- teractions with many targets. A notable exception is the MUV group, which has been processed with consideration of these pathologies (Rohrer & Baumann, 2009).\n# 3.2. Small Molecule Featurization", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
11
# 3.2. Small Molecule Featurization We used extended connectivity fingerprints (ECFP4) (Rogers & Hahn, 2010) generated by RDKit (Landrum) to featurize each molecule. The molecule is decomposed into a set of fragments—each centered at a non-hydrogen atom—where each fragment extends radially along bonds to neighboring atoms. Each fragment is assigned a unique identifier, and the collection of identifiers for a molecule is hashed into a fixed-length bit vector to construct the molec- ular “fingerprint”. ECFP4 and other fingerprints are com- monly used in cheminformatics applications, especially to measure similarity between compounds (Willett et al., 1998). A number of molecules (especially in the Tox21 group) failed the featurization process and were not used in training our networks. See the Appendix for details. K . 1 Mean / Median { K > AUC; (Dn)
1502.02072#11
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 11, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 3.2. Small Molecule Featurization\nWe used extended connectivity fingerprints (ECFP4) (Rogers & Hahn, 2010) generated by RDKit (Landrum) to featurize each molecule. The molecule is decomposed into a set of fragments—each centered at a non-hydrogen atom—where each fragment extends radially along bonds to neighboring atoms. Each fragment is assigned a unique identifier, and the collection of identifiers for a molecule is hashed into a fixed-length bit vector to construct the molec- ular “fingerprint”. ECFP4 and other fingerprints are com- monly used in cheminformatics applications, especially to measure similarity between compounds (Willett et al., 1998). A number of molecules (especially in the Tox21 group) failed the featurization process and were not used in training our networks. See the Appendix for details.\nK . 1 Mean / Median { K > AUC; (Dn)", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
12
K . 1 Mean / Median { K > AUC; (Dn) where AUCk(Dn) is defined as the AUC of a classifier trained on folds {1, . . . , K} \ k of dataset Dn and tested on fold k. For completeness, we include in the Appendix an alternative metric called “enrichment” that is widely used in the cheminformatics literature (Jain & Nicholls, 2008). We note that many other performance metrics exist in the literature; the lack of standard metrics makes it difficult to do direct comparisons with previous work. # 3.4. Multitask Networks A neural network is a nonlinear classifier that performs re- peated linear and nonlinear transformations on its input. Let xi represent the input to the i-th layer of the network (where x0 is simply the feature vector). The transformation performed is xi+1 = σ(Wixi + bi) # 3.3. Validation Scheme and Metrics
1502.02072#12
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 12, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "K . 1 Mean / Median { K > AUC; (Dn)\nwhere AUCk(Dn) is defined as the AUC of a classifier trained on folds {1, . . . , K} \\ k of dataset Dn and tested on fold k. For completeness, we include in the Appendix an alternative metric called “enrichment” that is widely used in the cheminformatics literature (Jain & Nicholls, 2008). We note that many other performance metrics exist in the literature; the lack of standard metrics makes it difficult to do direct comparisons with previous work.\n# 3.4. Multitask Networks\nA neural network is a nonlinear classifier that performs re- peated linear and nonlinear transformations on its input. Let xi represent the input to the i-th layer of the network (where x0 is simply the feature vector). The transformation performed is\nxi+1 = σ(Wixi + bi)\n# 3.3. Validation Scheme and Metrics", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
13
xi+1 = σ(Wixi + bi) # 3.3. Validation Scheme and Metrics The traditional approach for model evaluation is to have fixed training, validation, and test sets. However, the im- balance present in our datasets means that performance varies widely depending on the particular training/test split. To compensate for this variability, we used stratified K- fold cross-validation; that is, each fold maintains the ac- tive/inactive proportion present in the unsplit data. For the remainder of the paper, we use K = 5. Note that we did not choose an explicit validation set. Sev- eral datasets in our collection have very few actives (∼ 30 each for the MUV group), and we feared that selecting a specific validation set would skew our results. As a conse- quence, we suspect that our choice of hyperparameters may be affected by information leakage across folds. However, our networks do not appear to be highly sensitive to hyper- parameter choice (see Section 4.1), so we do not consider leakage to be a serious issue.
1502.02072#13
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 13, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "xi+1 = σ(Wixi + bi)\n# 3.3. Validation Scheme and Metrics\nThe traditional approach for model evaluation is to have fixed training, validation, and test sets. However, the im- balance present in our datasets means that performance varies widely depending on the particular training/test split. To compensate for this variability, we used stratified K- fold cross-validation; that is, each fold maintains the ac- tive/inactive proportion present in the unsplit data. For the remainder of the paper, we use K = 5.\nNote that we did not choose an explicit validation set. Sev- eral datasets in our collection have very few actives (∼ 30 each for the MUV group), and we feared that selecting a specific validation set would skew our results. As a conse- quence, we suspect that our choice of hyperparameters may be affected by information leakage across folds. However, our networks do not appear to be highly sensitive to hyper- parameter choice (see Section 4.1), so we do not consider leakage to be a serious issue.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
14
where Wi and bi are respectively the weight matrix and bias for the i-th layer, and σ is a nonlinearity (in our work, the rectified linear unit (Nair & Hinton, 2010)). After L such transformations, the final layer of the network xL is then fed to a simple linear classifier, such as the softmax, which predicts the probability that the input x0 has label j: . ew?) xi Ply = j|xo) Se where M is the number of possible labels (here M = 2) and w1, · · · , wM are weight vectors. Wi, bi, and wm are learned during training by the backpropagation algorithm (Rumelhart et al., 1988). A multitask network attaches N softmax classifiers, one for each task, to the final layer xL. (A “task” corresponds to the classifier associated with a particular dataset in our collection, although we often use “task” and “dataset” interchangeably. See Figure 1.) # 4. Experimental Section Following recommendations from the cheminformatics In this section, we seek to answer a number of questions about the performance, capabilities, and limitations of masMassively Multitask Networks for Drug Discovery
1502.02072#14
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 14, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "where Wi and bi are respectively the weight matrix and bias for the i-th layer, and σ is a nonlinearity (in our work, the rectified linear unit (Nair & Hinton, 2010)). After L such transformations, the final layer of the network xL is then fed to a simple linear classifier, such as the softmax, which predicts the probability that the input x0 has label j:\n. ew?) xi Ply = j|xo) Se\nwhere M is the number of possible labels (here M = 2) and w1, · · · , wM are weight vectors. Wi, bi, and wm are learned during training by the backpropagation algorithm (Rumelhart et al., 1988). A multitask network attaches N softmax classifiers, one for each task, to the final layer xL. (A “task” corresponds to the classifier associated with a particular dataset in our collection, although we often use “task” and “dataset” interchangeably. See Figure 1.)\n# 4. Experimental Section\nFollowing recommendations from the cheminformatics\nIn this section, we seek to answer a number of questions about the performance, capabilities, and limitations of masMassively Multitask Networks for Drug Discovery", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
15
Following recommendations from the cheminformatics In this section, we seek to answer a number of questions about the performance, capabilities, and limitations of masMassively Multitask Networks for Drug Discovery Softmax nodes, one per dataset Hidden layers. 1-4 layers with 50-3000 nodes Fully connected to layer below, rectified linear activation Figure 1. Multitask neural network. sively multitask neural networks: 1. Do massively multitask networks provide a perfor- mance boost over simple machine learning methods? If so, what is the optimal architecture for massively multitask networks? 2. How does the performance of a multitask network de- pend on the number of tasks? How does the perfor- mance depend on the total amount of data? with the networks overfitting the data. As discussed in Sec- tion 3.1, our datasets had a very small fraction of positive examples. For the single hidden layer multitask network in Table 2, each dataset had 1200 associated parameters. With a total number of positives in the tens or hundreds, overfitting this number of parameters is a major issue in the absence of strong regularization.
1502.02072#15
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 15, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Following recommendations from the cheminformatics\nIn this section, we seek to answer a number of questions about the performance, capabilities, and limitations of masMassively Multitask Networks for Drug Discovery\nSoftmax nodes, one per dataset Hidden layers. 1-4 layers with 50-3000 nodes Fully connected to layer below, rectified linear activation\nFigure 1. Multitask neural network.\nsively multitask neural networks:\n1. Do massively multitask networks provide a perfor- mance boost over simple machine learning methods? If so, what is the optimal architecture for massively multitask networks?\n2. How does the performance of a multitask network de- pend on the number of tasks? How does the perfor- mance depend on the total amount of data?\nwith the networks overfitting the data. As discussed in Sec- tion 3.1, our datasets had a very small fraction of positive examples. For the single hidden layer multitask network in Table 2, each dataset had 1200 associated parameters. With a total number of positives in the tens or hundreds, overfitting this number of parameters is a major issue in the absence of strong regularization.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
16
Reducing the number of parameters specific to each dataset In our is the motivation for the pyramidal architecture. pyramidal networks, the first hidden layer is very wide (2000 nodes) with a second narrow hidden layer (100 nodes). This dimensionality reduction is similar in moti- vation and implementation to the 1x1 convolutions in the GoogLeNet architecture (Szegedy et al., 2014). The wide lower layer allows for complex, expressive features to be learned while the narrow layer limits the parameters spe- cific to each task. Adding dropout of 0.25 to our pyramidal networks improved performance. We also trained single- task versions of our best pyramidal network to understand whether this design pattern works well with less data. Ta- ble 2 indicates that these models outperform vanilla single- task networks but do not substitute for multitask training. Results for a variety of alternate models are presented in the Appendix. 3. Do massively multitask networks extract generaliz- able information about chemical space? 4. When do datasets benefit from multitask training? The following subsections detail a series of experiments that seek to answer these questions.
1502.02072#16
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 16, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Reducing the number of parameters specific to each dataset In our is the motivation for the pyramidal architecture. pyramidal networks, the first hidden layer is very wide (2000 nodes) with a second narrow hidden layer (100 nodes). This dimensionality reduction is similar in moti- vation and implementation to the 1x1 convolutions in the GoogLeNet architecture (Szegedy et al., 2014). The wide lower layer allows for complex, expressive features to be learned while the narrow layer limits the parameters spe- cific to each task. Adding dropout of 0.25 to our pyramidal networks improved performance. We also trained single- task versions of our best pyramidal network to understand whether this design pattern works well with less data. Ta- ble 2 indicates that these models outperform vanilla single- task networks but do not substitute for multitask training. Results for a variety of alternate models are presented in the Appendix.\n3. Do massively multitask networks extract generaliz- able information about chemical space?\n4. When do datasets benefit from multitask training?\nThe following subsections detail a series of experiments that seek to answer these questions.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
17
4. When do datasets benefit from multitask training? The following subsections detail a series of experiments that seek to answer these questions. We investigated the sensitivity of our results to the sizes of the pyramidal layers by running networks with all com- binations of hidden layer sizes: (1000, 2000, 3000) and (50, 100, 150). Across the architectures, means and medi- ans shifted by ≤ .01 AUC with only MUV showing larger changes with a range of .038. We note that performance is sensitive to the choice of learning rate and the number of training steps. See the Appendix for details and data. # 4.1. Experimental Exploration of Massively Multitask Networks We investigate the performance of multitask networks with various hyperparameters and compare to several standard machine learning approaches. Table 2 shows some of the highlights of our experiments. Our best multitask archi- tecture (pyramidal multitask networks) significantly out- performed simpler models, including a hypothetical model whose performance on each dataset matches that of the best single-task model (Max{LR, RF, STNN, PSTNN}). # 4.2. Relationship between performance and number of tasks
1502.02072#17
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 17, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "4. When do datasets benefit from multitask training?\nThe following subsections detail a series of experiments that seek to answer these questions.\nWe investigated the sensitivity of our results to the sizes of the pyramidal layers by running networks with all com- binations of hidden layer sizes: (1000, 2000, 3000) and (50, 100, 150). Across the architectures, means and medi- ans shifted by ≤ .01 AUC with only MUV showing larger changes with a range of .038. We note that performance is sensitive to the choice of learning rate and the number of training steps. See the Appendix for details and data.\n# 4.1. Experimental Exploration of Massively Multitask Networks\nWe investigate the performance of multitask networks with various hyperparameters and compare to several standard machine learning approaches. Table 2 shows some of the highlights of our experiments. Our best multitask archi- tecture (pyramidal multitask networks) significantly out- performed simpler models, including a hypothetical model whose performance on each dataset matches that of the best single-task model (Max{LR, RF, STNN, PSTNN}).\n# 4.2. Relationship between performance and number of tasks", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
18
# 4.2. Relationship between performance and number of tasks The previous section demonstrated that massively multi- task networks improve performance over single-task mod- els. In this section, we seek to understand how multitask performance is affected by increasing the number of tasks. A priori, there are three reasonable “growth curves” (visu- ally represented in Figure 2): Every model we trained performed extremely well on the DUD-E datasets (all models in Table 2 had median 5- fold-average AUCs ≥ 0.99), making comparisons between models on DUD-E uninformative. For that reason, we exclude DUD-E from our subsequent statistical analysis. However, we did not remove DUD-E from the training alto- gether because doing so adversely affected performance on the other datasets (data not shown); we theorize that DUD- E helped to regularize the classifier and avoid overfitting. During our first explorations, we had consistent problems Over the hill: performance initially improves, hits a max- imum, then falls. Plateau: performance initially improves, then plateaus. Still climbing: performance improves throughout, but with a diminishing rate of return.
1502.02072#18
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 18, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 4.2. Relationship between performance and number of tasks\nThe previous section demonstrated that massively multi- task networks improve performance over single-task mod- els. In this section, we seek to understand how multitask performance is affected by increasing the number of tasks. A priori, there are three reasonable “growth curves” (visu- ally represented in Figure 2):\nEvery model we trained performed extremely well on the DUD-E datasets (all models in Table 2 had median 5- fold-average AUCs ≥ 0.99), making comparisons between models on DUD-E uninformative. For that reason, we exclude DUD-E from our subsequent statistical analysis. However, we did not remove DUD-E from the training alto- gether because doing so adversely affected performance on the other datasets (data not shown); we theorize that DUD- E helped to regularize the classifier and avoid overfitting.\nDuring our first explorations, we had consistent problems\nOver the hill: performance initially improves, hits a max- imum, then falls.\nPlateau: performance initially improves, then plateaus.\nStill climbing: performance improves throughout, but with a diminishing rate of return.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
19
Plateau: performance initially improves, then plateaus. Still climbing: performance improves throughout, but with a diminishing rate of return. We constructed and trained a series of multitask networks on datasets containing 10, 20, 40, 80, 160, and 249 tasks. These datasets all contain a fixed set of ten “held-in” tasks, which consists of a randomly sampled collection of five Massively Multitask Networks for Drug Discovery Table 2. Median 5-fold-average AUCs for various models. For each model, the sign test in the last column estimates the fraction of datasets (excluding the DUD-E group, for reasons discussed in the text) for which that model is superior to the PMTNN (bottom row). We use the Wilson score interval to derive a 95% confidence interval for this fraction. Non-neural network methods were trained using scikit-learn (Pedregosa et al., 2011) implementations and basic hyperparameter optimization. We also include results for a hypothetical “best” single-task model (Max{LR, RF, STNN, PSTNN}) to provide a stronger baseline. Details for our cross-validation and training procedures are given in the Appendix.
1502.02072#19
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 19, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Plateau: performance initially improves, then plateaus.\nStill climbing: performance improves throughout, but with a diminishing rate of return.\nWe constructed and trained a series of multitask networks on datasets containing 10, 20, 40, 80, 160, and 249 tasks. These datasets all contain a fixed set of ten “held-in” tasks, which consists of a randomly sampled collection of five\nMassively Multitask Networks for Drug Discovery\nTable 2. Median 5-fold-average AUCs for various models. For each model, the sign test in the last column estimates the fraction of datasets (excluding the DUD-E group, for reasons discussed in the text) for which that model is superior to the PMTNN (bottom row). We use the Wilson score interval to derive a 95% confidence interval for this fraction. Non-neural network methods were trained using scikit-learn (Pedregosa et al., 2011) implementations and basic hyperparameter optimization. We also include results for a hypothetical “best” single-task model (Max{LR, RF, STNN, PSTNN}) to provide a stronger baseline. Details for our cross-validation and training procedures are given in the Appendix.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
20
Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Sign Test CI Logistic Regression (LR) Random Forest (RF) Single-Task Neural Net (STNN) Pyramidal (2000, 100) STNN (PSTNN) Max{LR, RF, STNN, PSTNN} 1-Hidden (1200) Layer Multitask Neural Net (MTNN) Pyramidal (2000, 100) Multitask Neural Net (PMTNN) .801 .800 .795 .809 .824 .842 .873 .752 .774 .732 .745 .781 .797 .841 .738 .790 .714 .740 .790 .785 .818 [.04, .13] [.06, .16] [.04, .12] [.06, .16] [.12, .24] [.08, .18] Still climbing Plateau Over the hill Multitask Improvement Number of Tasks Figure 2. Potential multitask growth curves
1502.02072#20
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 20, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Sign Test CI Logistic Regression (LR) Random Forest (RF) Single-Task Neural Net (STNN) Pyramidal (2000, 100) STNN (PSTNN) Max{LR, RF, STNN, PSTNN} 1-Hidden (1200) Layer Multitask Neural Net (MTNN) Pyramidal (2000, 100) Multitask Neural Net (PMTNN) .801 .800 .795 .809 .824 .842 .873 .752 .774 .732 .745 .781 .797 .841 .738 .790 .714 .740 .790 .785 .818 [.04, .13] [.06, .16] [.04, .12] [.06, .16] [.12, .24] [.08, .18]\nStill climbing Plateau Over the hill Multitask Improvement Number of Tasks\nFigure 2. Potential multitask growth curves", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
21
Still climbing Plateau Over the hill Multitask Improvement Number of Tasks Figure 2. Potential multitask growth curves 0.12 Tox21 NR-Aromatase 0.08 MUV 852 pos sires 0.04 PCBA 485297 MUV 859 MUY 466 ———Tox2t'SR-MIMP PCBA 651644 EBA 743266 0.00 + - AUC from single task NN PCBA 899 -0.08 1020 40 80 160 249 # of Tasks PCBA, three MUV, and two Tox21 datasets. These datasets correspond to unique targets that do not have any obvious analogs in the remaining collection. (We also excluded a similarly chosen set of ten “held-out” tasks for use in Sec- tion 4.4). Each training collection is a superset of the pre- ceding collection, with tasks added randomly. For each net- work in the series, we computed the mean 5-fold-average- AUC for the tasks in the held-in collection. We repeated this experiment ten times with different choices of random seed.
1502.02072#21
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 21, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Still climbing Plateau Over the hill Multitask Improvement Number of Tasks\nFigure 2. Potential multitask growth curves\n0.12 Tox21 NR-Aromatase 0.08 MUV 852 pos sires 0.04 PCBA 485297 MUV 859 MUY 466 ———Tox2t'SR-MIMP PCBA 651644 EBA 743266 0.00 + - AUC from single task NN PCBA 899 -0.08 1020 40 80 160 249 # of Tasks\nPCBA, three MUV, and two Tox21 datasets. These datasets correspond to unique targets that do not have any obvious analogs in the remaining collection. (We also excluded a similarly chosen set of ten “held-out” tasks for use in Sec- tion 4.4). Each training collection is a superset of the pre- ceding collection, with tasks added randomly. For each net- work in the series, we computed the mean 5-fold-average- AUC for the tasks in the held-in collection. We repeated this experiment ten times with different choices of random seed.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
22
Figure 3. Held-in growth curves. The y-axis shows the change in AUC compared to a single-task neural network with the same architecture (PSTNN). Each colored curve is the multitask im- provement for a given held-in dataset. Black dots represent means across the 10 held-in datasets for each experimental run, where additional tasks were randomly selected. The shaded curve is the mean across the 100 combinations of datasets and experimental runs. Figure 3 plots the results of our experiments. The shaded region emphasizes the average growth curve, while black dots indicate average results for different experimental runs. The figure also displays lines associated with each held-in dataset. Note that several datasets show initial dips in performance. However, all datasets show subsequent im- provement, and all but one achieves performance superior to the single-task baseline. Within the limits of our current dataset collection, the distribution in Figure 3 agrees with either plateau or still climbing. The mean performance on the held-in set is still increasing at 249 tasks, so we hypothesize that performance is still climbing. It is possible that our collection is too small and that an alternate pattern may eventually emerge. # 4.3. More tasks or more data?
1502.02072#22
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 22, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Figure 3. Held-in growth curves. The y-axis shows the change in AUC compared to a single-task neural network with the same architecture (PSTNN). Each colored curve is the multitask im- provement for a given held-in dataset. Black dots represent means across the 10 held-in datasets for each experimental run, where additional tasks were randomly selected. The shaded curve is the mean across the 100 combinations of datasets and experimental runs.\nFigure 3 plots the results of our experiments. The shaded region emphasizes the average growth curve, while black dots indicate average results for different experimental runs. The figure also displays lines associated with each held-in dataset. Note that several datasets show initial dips in performance. However, all datasets show subsequent im- provement, and all but one achieves performance superior to the single-task baseline. Within the limits of our current dataset collection, the distribution in Figure 3 agrees with either plateau or still climbing. The mean performance on the held-in set is still increasing at 249 tasks, so we hypothesize that performance is still climbing. It is possible that our collection is too small and that an alternate pattern may eventually emerge.\n# 4.3. More tasks or more data?", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
23
# 4.3. More tasks or more data? In the previous section we studied the effects of adding more tasks, but here we investigate the relative importance the total number of tasks. of the total amount of data vs. Namely, is it better to have many tasks with a small amount of associated data, or a small number of tasks with a large amount of associated data? Massively Multitask Networks for Drug Discovery We constructed a series of multitask networks with 10, 15, 20, 30, 50 and 82 tasks. As in the previous section, the tasks are randomly associated with the networks in a cumulative manner (i.e., the 82-task network contained all tasks present in the 50-task network, and so on). All net- works contained the ten held-in tasks described in the pre- vious section. The 82 tasks chosen were associated with the largest datasets in our collection, each containing 300K- 500K data points. Note that all of these tasks belonged to the PCBA group.
1502.02072#23
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 23, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 4.3. More tasks or more data?\nIn the previous section we studied the effects of adding more tasks, but here we investigate the relative importance the total number of tasks. of the total amount of data vs. Namely, is it better to have many tasks with a small amount of associated data, or a small number of tasks with a large amount of associated data?\nMassively Multitask Networks for Drug Discovery\nWe constructed a series of multitask networks with 10, 15, 20, 30, 50 and 82 tasks. As in the previous section, the tasks are randomly associated with the networks in a cumulative manner (i.e., the 82-task network contained all tasks present in the 50-task network, and so on). All net- works contained the ten held-in tasks described in the pre- vious section. The 82 tasks chosen were associated with the largest datasets in our collection, each containing 300K- 500K data points. Note that all of these tasks belonged to the PCBA group.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
24
We then trained this series of networks multiple times with 1.6M, 3.3M, 6.5M, 13M, and 23M data points sampled from the non-held-in tasks. We perform the sampling such that for a given task, all data points present in the first stage (1.6M) appeared in the second (3.3M), all data points present in the second stage appeared in the third (6.5M), and so on. We decided to use larger datasets so we could sample meaningfully across this entire range. Some com- binations of tasks and data points were not realized; for instance, we did not have enough data to train a 20-task network with 23M additional data points. We repeated this experiment ten times using different random seeds. Figure 4 shows the results of our experiments. The x-axis tracks the number of additional tasks, while the y-axis dis- plays the improvement in performance for the held-in set relative to a multitask network trained only on the held-in data. When the total amount of data is fixed, having more tasks consistently yields improvement. Similarly, when the number of tasks is fixed, adding additional data consis- tently improves performance. Our results suggest that the total amount of data and the total number of tasks both con- tribute significantly to the multitask effect.
1502.02072#24
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 24, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "We then trained this series of networks multiple times with 1.6M, 3.3M, 6.5M, 13M, and 23M data points sampled from the non-held-in tasks. We perform the sampling such that for a given task, all data points present in the first stage (1.6M) appeared in the second (3.3M), all data points present in the second stage appeared in the third (6.5M), and so on. We decided to use larger datasets so we could sample meaningfully across this entire range. Some com- binations of tasks and data points were not realized; for instance, we did not have enough data to train a 20-task network with 23M additional data points. We repeated this experiment ten times using different random seeds.\nFigure 4 shows the results of our experiments. The x-axis tracks the number of additional tasks, while the y-axis dis- plays the improvement in performance for the held-in set relative to a multitask network trained only on the held-in data. When the total amount of data is fixed, having more tasks consistently yields improvement. Similarly, when the number of tasks is fixed, adding additional data consis- tently improves performance. Our results suggest that the total amount of data and the total number of tasks both con- tribute significantly to the multitask effect.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
25
0.03 peo # Additional Input Examples — 23M -- 13M — 65m -- 3.3M —- 1.6m Mean AUC 0.01 0.00 5 10 20 72 40 # Additional Tasks Figure 4. Multitask benefit from increasing tasks and data inde- pendently. As in Figure 2, we added randomly selected tasks (x- axis) to a fixed held-in set. A stratified random sampling scheme was applied to the additional tasks in order to achieve fixed total numbers of additional input examples (color, line type). White points indicate the mean over 10 experimental runs of ∆ mean- AUC over the initial network trained on the 10 held-in datasets. Color-filled areas and error bars describe the smoothed 95% con- fidence intervals. stronger as multitask networks were trained on more data. Large multitask networks exhibited better transferability, but the average effect even with 249 datasets was only ∼ .01 AUC. We hypothesize that the extent of this gen- eralizability is determined by the presence or absence of relevant data in the multitask training set. # 4.5. When do datasets benefit from multitask training? # 4.4. Do massively multitask networks extract generalizable features?
1502.02072#25
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 25, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "0.03 peo # Additional Input Examples — 23M -- 13M — 65m -- 3.3M —- 1.6m Mean AUC 0.01 0.00 5 10 20 72 40 # Additional Tasks\nFigure 4. Multitask benefit from increasing tasks and data inde- pendently. As in Figure 2, we added randomly selected tasks (x- axis) to a fixed held-in set. A stratified random sampling scheme was applied to the additional tasks in order to achieve fixed total numbers of additional input examples (color, line type). White points indicate the mean over 10 experimental runs of ∆ mean- AUC over the initial network trained on the 10 held-in datasets. Color-filled areas and error bars describe the smoothed 95% con- fidence intervals.\nstronger as multitask networks were trained on more data. Large multitask networks exhibited better transferability, but the average effect even with 249 datasets was only ∼ .01 AUC. We hypothesize that the extent of this gen- eralizability is determined by the presence or absence of relevant data in the multitask training set.\n# 4.5. When do datasets benefit from multitask training?\n# 4.4. Do massively multitask networks extract generalizable features?", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
26
# 4.5. When do datasets benefit from multitask training? # 4.4. Do massively multitask networks extract generalizable features? The features extracted by the top layer of the network rep- resent information useful to many tasks. Consequently, we sought to determine the transferability of these features to tasks not in the training set. We held out ten data sets from the growth curves calculated in Section 4.2 and used the learned weights from points along the growth curves to ini- tialize single-task networks for the held-out datasets, which we then fine-tuned. The results in Sections 4.2 and 4.4 indicate that some datasets benefit more from multitask training than others. In an effort to explain these differences, we consider three specific questions: 1. Do shared active compounds explain multitask im- provement? 2. Do some biological target classes realize greater mul- titask improvement than others? The results of training these networks (with 5-fold strat- ified cross-validation) are shown in Figure 5. First, note that many of the datasets performed worse than the baseline when initialized from the 10-held-in-task networks. Fur- ther, some datasets never exhibited any positive effect due to multitask initialization. Transfer learning can be nega- tive. Second, note that the transfer learning effect became
1502.02072#26
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 26, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 4.5. When do datasets benefit from multitask training?\n# 4.4. Do massively multitask networks extract generalizable features?\nThe features extracted by the top layer of the network rep- resent information useful to many tasks. Consequently, we sought to determine the transferability of these features to tasks not in the training set. We held out ten data sets from the growth curves calculated in Section 4.2 and used the learned weights from points along the growth curves to ini- tialize single-task networks for the held-out datasets, which we then fine-tuned.\nThe results in Sections 4.2 and 4.4 indicate that some datasets benefit more from multitask training than others. In an effort to explain these differences, we consider three specific questions:\n1. Do shared active compounds explain multitask im- provement?\n2. Do some biological target classes realize greater mul- titask improvement than others?\nThe results of training these networks (with 5-fold strat- ified cross-validation) are shown in Figure 5. First, note that many of the datasets performed worse than the baseline when initialized from the 10-held-in-task networks. Fur- ther, some datasets never exhibited any positive effect due to multitask initialization. Transfer learning can be nega- tive.\nSecond, note that the transfer learning effect became", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
27
Second, note that the transfer learning effect became 3. Do tasks associated with duplicated targets have arti- ficially high multitask performance? 4.5.1. SHARED ACTIVE COMPOUNDS The biological context of our datasets implies that active compounds contain more information than inactive com- pounds; while an inactive compound may be inactive for many reasons, active compounds often rely on similar physical mechanisms. Hence, shared active compounds should be a good measure of dataset similarity. Massively Multitask Networks for Drug Discovery PBA 602233 __ —— Tox21 SR-ATADS LO MuV 832 POBA gz4at7 =f saee° PCBA 2675 —PCBA 1461 Tox21 NR-ARR, 0.05 0.004 - -0.05 MUV 548 ZAUC from single task NN -0.10 1020 40 249 80 160 # of Tasks/Datasets
1502.02072#27
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 27, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Second, note that the transfer learning effect became\n3. Do tasks associated with duplicated targets have arti- ficially high multitask performance?\n4.5.1. SHARED ACTIVE COMPOUNDS\nThe biological context of our datasets implies that active compounds contain more information than inactive com- pounds; while an inactive compound may be inactive for many reasons, active compounds often rely on similar physical mechanisms. Hence, shared active compounds should be a good measure of dataset similarity.\nMassively Multitask Networks for Drug Discovery\nPBA 602233 __ —— Tox21 SR-ATADS LO MuV 832 POBA gz4at7 =f saee° PCBA 2675 —PCBA 1461 Tox21 NR-ARR, 0.05 0.004 - -0.05 MUV 548 ZAUC from single task NN -0.10 1020 40 249 80 160 # of Tasks/Datasets", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
28
Figure 5. Held-out growth curves. The y-axis shows the change in AUC compared to a single-task neural network with the same architecture (PSTNN). Each colored curve is the result of initializ- ing a single-task neural network from the weights of the networks from Section 4.2 and computing the mean across the 10 experi- mental runs. These datasets were not included in the training of the original networks. The shaded curve is the mean across the 100 combinations of datasets and experimental runs, and black dots represent means across the 10 held-out datasets for each ex- perimental run, where additional tasks were randomly selected. Figure 6 plots multitask improvement against a measure of dataset similarity we call “active occurrence rate” (AOR). For each active compound α in dataset Di, AORi,α is de- fined as the number of additional datasets in which this compound is also active:
1502.02072#28
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 28, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Figure 5. Held-out growth curves. The y-axis shows the change in AUC compared to a single-task neural network with the same architecture (PSTNN). Each colored curve is the result of initializ- ing a single-task neural network from the weights of the networks from Section 4.2 and computing the mean across the 10 experi- mental runs. These datasets were not included in the training of the original networks. The shaded curve is the mean across the 100 combinations of datasets and experimental runs, and black dots represent means across the 10 held-out datasets for each ex- perimental run, where additional tasks were randomly selected.\nFigure 6 plots multitask improvement against a measure of dataset similarity we call “active occurrence rate” (AOR). For each active compound α in dataset Di, AORi,α is de- fined as the number of additional datasets in which this compound is also active:", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
29
There is a moderate correlation between AOR and ∆ log- odds-mean-AUC (r2 = .33); we note that this correlation is not present when we use ∆ mean-AUC as the y-coordinate (r2 = .09). We hypothesize that some portion of the multi- task effect is determined by shared active compounds. That is, a dataset is most likely to benefit from multitask training when it shares many active compounds with other datasets in the collection. 20 @ ~=PCBA e muy © Tox21 A Log-odds-mean-AUC 5 10 15 20 Active occurrence rate (AOR, ,.) Figure 6. Multitask improvement compared to active occurrence rate (AOR). Each point in the figure represents a particular dataset Di. The x-coordinate is the mean AOR across all active com- pounds in Di, and the y-coordinate is the difference in log-odds- mean-AUC between multitask and single-task models. The gray bars indicate standard deviations around the AOR means. There is a moderate correlation (r2 = .33). For reasons discussed in Section 4.1, we excluded DUD-E from this analysis. (Including DUD-E results in a similar correlation, r2 = .22.)
1502.02072#29
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 29, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "There is a moderate correlation between AOR and ∆ log- odds-mean-AUC (r2 = .33); we note that this correlation is not present when we use ∆ mean-AUC as the y-coordinate (r2 = .09). We hypothesize that some portion of the multi- task effect is determined by shared active compounds. That is, a dataset is most likely to benefit from multitask training when it shares many active compounds with other datasets in the collection.\n20 @ ~=PCBA e muy © Tox21 A Log-odds-mean-AUC 5 10 15 20 Active occurrence rate (AOR, ,.)\nFigure 6. Multitask improvement compared to active occurrence rate (AOR). Each point in the figure represents a particular dataset Di. The x-coordinate is the mean AOR across all active com- pounds in Di, and the y-coordinate is the difference in log-odds- mean-AUC between multitask and single-task models. The gray bars indicate standard deviations around the AOR means. There is a moderate correlation (r2 = .33). For reasons discussed in Section 4.1, we excluded DUD-E from this analysis. (Including DUD-E results in a similar correlation, r2 = .22.)", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
30
AORi,a = Ss 1(a € Actives (Da)). di Each point in Figure 6 corresponds to a single dataset Di. The x-coordinate is AORi = Mean α∈Actives(Di) (AORi,α) , and the y-coordinate (∆ log-odds-mean-AUC) is # 4.5.2. TARGET CLASSES Figure 7 shows the relationship between multitask im- provement and target classes. As before, we report mul- titask improvement in terms of log-odds and exclude the DUD-E datasets. Qualitatively, no target class benefited more than any other from multitask training. Nearly ev- ery target class realized gains, suggesting that the multitask framework is applicable to experimental data from multiple target classes. K K _{ 1 J _f 1 s logit (i » auc”? ») —logit G » auc\”) »)
1502.02072#30
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 30, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "AORi,a = Ss 1(a € Actives (Da)). di\nEach point in Figure 6 corresponds to a single dataset Di. The x-coordinate is\nAORi = Mean α∈Actives(Di) (AORi,α) ,\nand the y-coordinate (∆ log-odds-mean-AUC) is\n# 4.5.2. TARGET CLASSES\nFigure 7 shows the relationship between multitask im- provement and target classes. As before, we report mul- titask improvement in terms of log-odds and exclude the DUD-E datasets. Qualitatively, no target class benefited more than any other from multitask training. Nearly ev- ery target class realized gains, suggesting that the multitask framework is applicable to experimental data from multiple target classes.\nK K _{ 1 J _f 1 s logit (i » auc”? ») —logit G » auc\\”) »)", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
31
K K _{ 1 J _f 1 s logit (i » auc”? ») —logit G » auc\”) ») where AUC(M ) (Di) are respectively the AUC values for the k-th fold of dataset i in the multitask and single-task models, and logit (p) = log (p/(1 − p)). The use of log-odds reduces the effect of outliers and em- phasizes changes in AUC when the baseline is high. Note that for reasons discussed in Section 4.1, DUD-E was ex- cluded from this analysis. , # 4.5.3. DUPLICATE TARGETS As mentioned in Section 3.1, there are many cases of tasks with identical targets. We compared the multitask improve- ment of duplicate vs. unique tasks. The distributions have substantial overlap (see the Appendix), but the average log- odds improvement was slightly higher for duplicated tasks .372; a one-sided t-test between the duplicate (.531 vs. and unique distributions gave p = .016). Since duplicated targets are likely to share many active compounds, this im- provement is consistent with the correlation seen in SecMassively Multitask Networks for Drug Discovery \ ao) ro ens a Neos one ee ih do Ko 09 Target Class
1502.02072#31
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 31, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "K K _{ 1 J _f 1 s logit (i » auc”? ») —logit G » auc\\”) »)\nwhere AUC(M ) (Di) are respectively the AUC values for the k-th fold of dataset i in the multitask and single-task models, and logit (p) = log (p/(1 − p)). The use of log-odds reduces the effect of outliers and em- phasizes changes in AUC when the baseline is high. Note that for reasons discussed in Section 4.1, DUD-E was ex- cluded from this analysis.\n,\n# 4.5.3. DUPLICATE TARGETS\nAs mentioned in Section 3.1, there are many cases of tasks with identical targets. We compared the multitask improve- ment of duplicate vs. unique tasks. The distributions have substantial overlap (see the Appendix), but the average log- odds improvement was slightly higher for duplicated tasks .372; a one-sided t-test between the duplicate (.531 vs. and unique distributions gave p = .016). Since duplicated targets are likely to share many active compounds, this im- provement is consistent with the correlation seen in SecMassively Multitask Networks for Drug Discovery\n\\ ao) ro ens a Neos one ee ih do Ko 09 Target Class", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
32
\ ao) ro ens a Neos one ee ih do Ko 09 Target Class Figure 7. Multitask improvement across target classes. The x- coordinate lists a series of biological target classes represented in our dataset collection, and the y-coordinate is the difference in log-odds-mean-AUC between multitask and single-task models. Note that the DUD-E datasets are excluded. Classes are ordered by total number of targets (in parenthesis), and target classes with fewer than five members are merged into “miscellaneous.” tion 4.5.1. However, sign tests for single-task vs. multitask models for duplicate and unique targets gave significant and highly overlapping confidence intervals ([0.04, 0.24] and [0.06, 0.17], respectively; recall that the meaning of these intervals is given in the caption for Table 2). Together, these results suggest that there is not significant informa- tion leakage within multitask networks. Consequently, the results of our analysis are unlikely to be significantly af- fected by the presence of duplicate targets in our dataset collection. # 5. Discussion and Conclusion
1502.02072#32
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 32, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "\\ ao) ro ens a Neos one ee ih do Ko 09 Target Class\nFigure 7. Multitask improvement across target classes. The x- coordinate lists a series of biological target classes represented in our dataset collection, and the y-coordinate is the difference in log-odds-mean-AUC between multitask and single-task models. Note that the DUD-E datasets are excluded. Classes are ordered by total number of targets (in parenthesis), and target classes with fewer than five members are merged into “miscellaneous.”\ntion 4.5.1. However, sign tests for single-task vs. multitask models for duplicate and unique targets gave significant and highly overlapping confidence intervals ([0.04, 0.24] and [0.06, 0.17], respectively; recall that the meaning of these intervals is given in the caption for Table 2). Together, these results suggest that there is not significant informa- tion leakage within multitask networks. Consequently, the results of our analysis are unlikely to be significantly af- fected by the presence of duplicate targets in our dataset collection.\n# 5. Discussion and Conclusion", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
33
# 5. Discussion and Conclusion We observed that the multitask effect was stronger for some datasets than others. Consequently, we investigated possible explanations for this discrepancy and found that the presence of shared active compounds was moderately correlated with multitask improvement, but the biological class of the target was not. It is also possible that multitask improvement results from accurately modeling experimen- tal artifacts rather than specific interactions between targets and small molecules. We do not believe this to be the case, as we demonstrated strong improvement on the thoroughly- cleaned MUV datasets. The efficacy of multitask learning is directly related to the availability of relevant data. Hence, obtaining greater amounts of data is of critical importance for improving the state of the art. Major pharmaceutical companies pos- sess vast private stores of experimental measurements; our work provides a strong argument that increased data shar- ing could result in benefits for all. More data will maximize the benefits achievable using cur- rent architectures, but in order for algorithmic progress to occur, it must be possible to judge the performance of pro- posed models against previous work. It is disappointing to note that all published applications of deep learning to vir- tual screening (that we are aware of) use distinct datasets that are not directly comparable. It remains to future re- search to establish standard datasets and performance met- rics for this field.
1502.02072#33
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 33, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# 5. Discussion and Conclusion\nWe observed that the multitask effect was stronger for some datasets than others. Consequently, we investigated possible explanations for this discrepancy and found that the presence of shared active compounds was moderately correlated with multitask improvement, but the biological class of the target was not. It is also possible that multitask improvement results from accurately modeling experimen- tal artifacts rather than specific interactions between targets and small molecules. We do not believe this to be the case, as we demonstrated strong improvement on the thoroughly- cleaned MUV datasets.\nThe efficacy of multitask learning is directly related to the availability of relevant data. Hence, obtaining greater amounts of data is of critical importance for improving the state of the art. Major pharmaceutical companies pos- sess vast private stores of experimental measurements; our work provides a strong argument that increased data shar- ing could result in benefits for all.\nMore data will maximize the benefits achievable using cur- rent architectures, but in order for algorithmic progress to occur, it must be possible to judge the performance of pro- posed models against previous work. It is disappointing to note that all published applications of deep learning to vir- tual screening (that we are aware of) use distinct datasets that are not directly comparable. It remains to future re- search to establish standard datasets and performance met- rics for this field.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
34
Another direction for future work is the further study of In this work, we use only small molecule featurization. one possible featurization (ECFP4), but there exist many others. Additional performance may also be realized by considering targets as well as small molecules in the fea- turization. Yet another line of research could improve per- formance by using unsupervised learning to explore much larger segments of chemical space. In this work, we investigated the use of massively multitask networks for virtual screening. We gathered a large collec- tion of publicly available experimental data that we used to train massively multitask neural networks. These net- works achieved significant improvement over simple ma- chine learning algorithms. Although deep learning offers interesting possibilities for virtual screening, the full drug discovery process remains immensely complicated. Can deep learning—coupled with large amounts of experimental data—trigger a revolution in this field? Considering the transformational effect that these methods have had on other fields, we are optimistic about the future.
1502.02072#34
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 34, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Another direction for future work is the further study of In this work, we use only small molecule featurization. one possible featurization (ECFP4), but there exist many others. Additional performance may also be realized by considering targets as well as small molecules in the fea- turization. Yet another line of research could improve per- formance by using unsupervised learning to explore much larger segments of chemical space.\nIn this work, we investigated the use of massively multitask networks for virtual screening. We gathered a large collec- tion of publicly available experimental data that we used to train massively multitask neural networks. These net- works achieved significant improvement over simple ma- chine learning algorithms.\nAlthough deep learning offers interesting possibilities for virtual screening, the full drug discovery process remains immensely complicated. Can deep learning—coupled with large amounts of experimental data—trigger a revolution in this field? Considering the transformational effect that these methods have had on other fields, we are optimistic about the future.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
35
We explored several aspects of the multitask framework. First, we demonstrated that multitask performance im- proved with the addition of more tasks; our performance was still climbing at 259 tasks. Next, we considered the rel- ative importance of introducing more data vs. more tasks. We found that additional data and additional tasks both contributed significantly to the multitask effect. We next discovered that multitask learning afforded limited trans- ferability to tasks not contained in the training set. This ef- fect was not universal, and required large amounts of data even when it did apply. # Acknowledgments B.R. was supported by the Fannie and John Hertz Foun- dation. S.K. was supported by a Smith Stanford Graduate Fellowship. We also acknowledge support from NIH and NSF, in particular NIH U54 GM072970 and NSF 0960306. The latter award was funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Massively Multitask Networks for Drug Discovery # References Abdo, Ammar, Chen, Beining, Mueller, Christoph, Salim, Naomie, and Willett, Peter. Ligand-based virtual screen- ing using bayesian networks. Journal of chemical infor- mation and modeling, 50(6):1012–1020, 2010.
1502.02072#35
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 35, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "We explored several aspects of the multitask framework. First, we demonstrated that multitask performance im- proved with the addition of more tasks; our performance was still climbing at 259 tasks. Next, we considered the rel- ative importance of introducing more data vs. more tasks. We found that additional data and additional tasks both contributed significantly to the multitask effect. We next discovered that multitask learning afforded limited trans- ferability to tasks not contained in the training set. This ef- fect was not universal, and required large amounts of data even when it did apply.\n# Acknowledgments\nB.R. was supported by the Fannie and John Hertz Foun- dation. S.K. was supported by a Smith Stanford Graduate Fellowship. We also acknowledge support from NIH and NSF, in particular NIH U54 GM072970 and NSF 0960306. The latter award was funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).\nMassively Multitask Networks for Drug Discovery\n# References\nAbdo, Ammar, Chen, Beining, Mueller, Christoph, Salim, Naomie, and Willett, Peter. Ligand-based virtual screen- ing using bayesian networks. Journal of chemical infor- mation and modeling, 50(6):1012–1020, 2010.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
36
Collobert, Ronan and Weston, Jason. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th inter- national conference on Machine learning, pp. 160–167. ACM, 2008. Nair, Vinod and Hinton, Geoffrey E. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learn- ing (ICML-10), pp. 807–814, 2010. Pedregosa, Fabian, Varoquaux, Ga¨el, Gramfort, Alexan- dre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, Dubourg, Vincent, et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825–2830, 2011. Dahl, George. Deep Learning How I Did It: Merck 1st place interview. No Free Hunch, November 1, 2012. Rogers, David and Hahn, Mathew. Extended-connectivity fingerprints. Journal of chemical information and mod- eling, 50(5):742–754, 2010.
1502.02072#36
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 36, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Collobert, Ronan and Weston, Jason. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th inter- national conference on Machine learning, pp. 160–167. ACM, 2008.\nNair, Vinod and Hinton, Geoffrey E. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learn- ing (ICML-10), pp. 807–814, 2010.\nPedregosa, Fabian, Varoquaux, Ga¨el, Gramfort, Alexan- dre, Michel, Vincent, Thirion, Bertrand, Grisel, Olivier, Blondel, Mathieu, Prettenhofer, Peter, Weiss, Ron, Dubourg, Vincent, et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825–2830, 2011.\nDahl, George. Deep Learning How I Did It: Merck 1st place interview. No Free Hunch, November 1, 2012.\nRogers, David and Hahn, Mathew. Extended-connectivity fingerprints. Journal of chemical information and mod- eling, 50(5):742–754, 2010.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
37
Dahl, George E, Jaitly, Navdeep, and Salakhutdinov, Rus- lan. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231, 2014. Deng, Li, Hinton, Geoffrey, and Kingsbury, Brian. New types of deep neural network learning for speech recog- nition and related applications: An overview. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp. 8599–8603. IEEE, 2013. Rohrer, Sebastian G and Baumann, Knut. Maximum un- biased validation (MUV) data sets for virtual screening based on pubchem bioactivity data. Journal of chemical information and modeling, 49(2):169–184, 2009. Rumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J. Learning representations by back-propagating errors. Cognitive modeling, 1988. Erhan, Dumitru, L’Heureux, Pierre-Jean, Yue, Shi Yi, and Bengio, Yoshua. Collaborative filtering on a family of biological targets. Journal of chemical information and modeling, 46(2):626–635, 2006.
1502.02072#37
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 37, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Dahl, George E, Jaitly, Navdeep, and Salakhutdinov, Rus- lan. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231, 2014.\nDeng, Li, Hinton, Geoffrey, and Kingsbury, Brian. New types of deep neural network learning for speech recog- nition and related applications: An overview. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp. 8599–8603. IEEE, 2013.\nRohrer, Sebastian G and Baumann, Knut. Maximum un- biased validation (MUV) data sets for virtual screening based on pubchem bioactivity data. Journal of chemical information and modeling, 49(2):169–184, 2009.\nRumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J. Learning representations by back-propagating errors. Cognitive modeling, 1988.\nErhan, Dumitru, L’Heureux, Pierre-Jean, Yue, Shi Yi, and Bengio, Yoshua. Collaborative filtering on a family of biological targets. Journal of chemical information and modeling, 46(2):626–635, 2006.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
38
Recommenda- tions for evaluation of computational methods. Journal of computer-aided molecular design, 22(3-4):133–139, 2008. Shoichet, Brian K. Virtual screening of chemical libraries. Nature, 432(7019):862–865, 2004. Swamidass, S Joshua, Azencott, Chlo´e-Agathe, Lin, Ting- Wan, Gramajo, Hugo, Tsai, Shiou-Chuan, and Baldi, Pierre. Influence relevance voting: an accurate and inter- pretable virtual high throughput screening method. Jour- nal of chemical information and modeling, 49(4):756– 766, 2009. Landrum, Greg. RDKit: Open-source cheminformatics. URL http://www.rdkit.org. Lowe, Derek. Did Kaggle Predict Drug Candidate Activi- ties? Or Not? In the Pipeline, December 11, 2012. Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Du- mitru, Vanhoucke, Vincent, and Rabinovich, Andrew. arXiv preprint Going deeper with convolutions. arXiv:1409.4842, 2014.
1502.02072#38
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 38, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Recommenda- tions for evaluation of computational methods. Journal of computer-aided molecular design, 22(3-4):133–139, 2008.\nShoichet, Brian K. Virtual screening of chemical libraries. Nature, 432(7019):862–865, 2004.\nSwamidass, S Joshua, Azencott, Chlo´e-Agathe, Lin, Ting- Wan, Gramajo, Hugo, Tsai, Shiou-Chuan, and Baldi, Pierre. Influence relevance voting: an accurate and inter- pretable virtual high throughput screening method. Jour- nal of chemical information and modeling, 49(4):756– 766, 2009.\nLandrum, Greg. RDKit: Open-source cheminformatics. URL http://www.rdkit.org.\nLowe, Derek. Did Kaggle Predict Drug Candidate Activi- ties? Or Not? In the Pipeline, December 11, 2012.\nSzegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Du- mitru, Vanhoucke, Vincent, and Rabinovich, Andrew. arXiv preprint Going deeper with convolutions. arXiv:1409.4842, 2014.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
39
Lusci, Alessandro, Pollastri, Gianluca, and Baldi, Pierre. Deep architectures and deep learning in chemoinformat- ics: the prediction of aqueous solubility for drug-like molecules. Journal of chemical information and mod- eling, 53(7):1563–1575, 2013. Unterthiner, Thomas, Mayr, Andreas, ¨unter Klambauer, G, Steijaert, Marvin, Wenger, J¨org, Ceulemans, Hugo, and Hochreiter, Sepp. Deep learning as an opportunity in virtual screening. Ma, Junshui, Sheridan, Robert P, Liaw, Andy, Dahl, George, and Svetnik, Vladimir. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 2015. Varnek, Alexandre and Baskin, Igor. Machine learning methods for property prediction in chemoinformatics: quo vadis? Journal of chemical information and model- ing, 52(6):1413–1437, 2012. Mysinger, Michael M, Carchia, Michael, Irwin, John J, and Shoichet, Brian K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better bench- marking. Journal of medicinal chemistry, 55(14):6582– 6594, 2012.
1502.02072#39
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 39, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Lusci, Alessandro, Pollastri, Gianluca, and Baldi, Pierre. Deep architectures and deep learning in chemoinformat- ics: the prediction of aqueous solubility for drug-like molecules. Journal of chemical information and mod- eling, 53(7):1563–1575, 2013.\nUnterthiner, Thomas, Mayr, Andreas, ¨unter Klambauer, G, Steijaert, Marvin, Wenger, J¨org, Ceulemans, Hugo, and Hochreiter, Sepp. Deep learning as an opportunity in virtual screening.\nMa, Junshui, Sheridan, Robert P, Liaw, Andy, Dahl, George, and Svetnik, Vladimir. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 2015.\nVarnek, Alexandre and Baskin, Igor. Machine learning methods for property prediction in chemoinformatics: quo vadis? Journal of chemical information and model- ing, 52(6):1413–1437, 2012.\nMysinger, Michael M, Carchia, Michael, Irwin, John J, and Shoichet, Brian K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better bench- marking. Journal of medicinal chemistry, 55(14):6582– 6594, 2012.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
40
Wang, Yanli, Xiao, Jewen, Suzek, Tugba O, Zhang, Jian, Wang, Jiyao, Zhou, Zhigang, Han, Lianyi, Karapetyan, Karen, Dracheva, Svetlana, Shoemaker, Benjamin A, et al. PubChem’s BioAssay database. Nucleic acids re- search, 40(D1):D400–D412, 2012. Massively Multitask Networks for Drug Discovery Willett, Peter, Barnard, John M, and Downs, Geoffrey M. Chemical similarity searching. Journal of chemical in- formation and computer sciences, 38(6):983–996, 1998. # Massively Multitask Networks for Drug Discovery: Appendix # February 11, 2022 # A. Dataset Construction and Design
1502.02072#40
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 40, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Wang, Yanli, Xiao, Jewen, Suzek, Tugba O, Zhang, Jian, Wang, Jiyao, Zhou, Zhigang, Han, Lianyi, Karapetyan, Karen, Dracheva, Svetlana, Shoemaker, Benjamin A, et al. PubChem’s BioAssay database. Nucleic acids re- search, 40(D1):D400–D412, 2012.\nMassively Multitask Networks for Drug Discovery\nWillett, Peter, Barnard, John M, and Downs, Geoffrey M. Chemical similarity searching. Journal of chemical in- formation and computer sciences, 38(6):983–996, 1998.\n# Massively Multitask Networks for Drug Discovery: Appendix\n# February 11, 2022\n# A. Dataset Construction and Design", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
41
# Massively Multitask Networks for Drug Discovery: Appendix # February 11, 2022 # A. Dataset Construction and Design The PCBA datasets are dose-response assays performed by the NCATS Chemical Genomics Center (NCGC) and down- loaded from PubChem BioAssay using the following search limits: TotalSidCount from 10000, ActiveSidCount from 30, Chemical, Confirmatory, Dose-Response, Target: Single, NCGC. These limits correspond to the search query: (10000[TotalSidCount] : 1000000000[TotalSidCount]) AND (30[ActiveSidCount] : 1000000000[ActiveSidCount]) AND “small molecule”[filt] AND “doseresponse”[filt] AND 1[TargetCount] AND “NCGC”[SourceName]. We note that the DUD-E datasets are especially susceptible to “artificial enrichment” (unrealistic divisions between active and inactive compounds) as an artifact of the dataset construction procedure. Each data point in our collection was associated with a binary label classifying it as either active or inactive.
1502.02072#41
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 41, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "# Massively Multitask Networks for Drug Discovery: Appendix\n# February 11, 2022\n# A. Dataset Construction and Design\nThe PCBA datasets are dose-response assays performed by the NCATS Chemical Genomics Center (NCGC) and down- loaded from PubChem BioAssay using the following search limits: TotalSidCount from 10000, ActiveSidCount from 30, Chemical, Confirmatory, Dose-Response, Target: Single, NCGC. These limits correspond to the search query: (10000[TotalSidCount] : 1000000000[TotalSidCount]) AND (30[ActiveSidCount] : 1000000000[ActiveSidCount]) AND “small molecule”[filt] AND “doseresponse”[filt] AND 1[TargetCount] AND “NCGC”[SourceName]. We note that the DUD-E datasets are especially susceptible to “artificial enrichment” (unrealistic divisions between active and inactive compounds) as an artifact of the dataset construction procedure. Each data point in our collection was associated with a binary label classifying it as either active or inactive.", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
42
A description of each of our 259 datasets is given in Table A1. These datasets cover a wide range of target classes and assay types, including both cell-based and in vitro experiments. Datasets with duplicated targets are marked with an asterisk (note that only the non-DUD-E duplicate target datasets were used in the analysis described in the text). For the PCBA datasets, compounds not labeled “Active” were considered inactive (including compounds marked “Inconclusive”). Due to missing data in PubChem BioAssay and/or featurization errors, some data points and compounds were not used for evaluation of our models; failure rates for each dataset group are shown in Table A.2. The Tox21 group suffered especially high failure rates, likely due to the relatively large number of metallic or otherwise abnormal compounds that are not supported by the RDKit package. The counts given in Table A1 do not include these missing data. A graphical breakdown of the datasets by target class is shown in Figure A.1. The datasets used for the held-in and held-out analyses are repeated in Table A.3 and Table A.4, respectively. As an extension of our treatment of task similarity in the text, we generated the heatmap in Figure A.2 to show the pairwise intersection between all datasets in our collection. A few characteristics of our datasets are immediately apparent:
1502.02072#42
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 42, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "A description of each of our 259 datasets is given in Table A1. These datasets cover a wide range of target classes and assay types, including both cell-based and in vitro experiments. Datasets with duplicated targets are marked with an asterisk (note that only the non-DUD-E duplicate target datasets were used in the analysis described in the text). For the PCBA datasets, compounds not labeled “Active” were considered inactive (including compounds marked “Inconclusive”). Due to missing data in PubChem BioAssay and/or featurization errors, some data points and compounds were not used for evaluation of our models; failure rates for each dataset group are shown in Table A.2. The Tox21 group suffered especially high failure rates, likely due to the relatively large number of metallic or otherwise abnormal compounds that are not supported by the RDKit package. The counts given in Table A1 do not include these missing data. A graphical breakdown of the datasets by target class is shown in Figure A.1. The datasets used for the held-in and held-out analyses are repeated in Table A.3 and Table A.4, respectively.\nAs an extension of our treatment of task similarity in the text, we generated the heatmap in Figure A.2 to show the pairwise intersection between all datasets in our collection. A few characteristics of our datasets are immediately apparent:", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
43
• The datasets in the DUD-E group have very little intersection with any other datasets. • The PCBA and Tox21 datasets have substantial self-overlap. In contrast, the MUV datasets have relatively little self-overlap. • The MUV datasets have substantial overlap with the datasets in the PCBA group. • The Tox21 datasets have very small intersections with datasets in other groups. Figure A.3 shows the ∆ log-odds-mean-AUC for datasets with duplicate and unique targets. Dataset Actives Inactives Target Class Target pcba-aid411* pcba-aid875 pcba-aid881 pcba-aid883 pcba-aid884 pcba-aid885 1562 32 589 1214 3391 163 69 734 73 870 106 656 8170 9676 12 904 other enzyme protein-protein interaction other enzyme other enzyme other enzyme other enzyme luciferase brca1-bach1 15hLO-2 CYP2C9 CYP3A4 CYP3A4 Massively Multitask Networks for Drug Discovery
1502.02072#43
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 43, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "• The datasets in the DUD-E group have very little intersection with any other datasets.\n• The PCBA and Tox21 datasets have substantial self-overlap. In contrast, the MUV datasets have relatively little self-overlap.\n• The MUV datasets have substantial overlap with the datasets in the PCBA group.\n• The Tox21 datasets have very small intersections with datasets in other groups.\nFigure A.3 shows the ∆ log-odds-mean-AUC for datasets with duplicate and unique targets.\nDataset Actives Inactives Target Class Target pcba-aid411* pcba-aid875 pcba-aid881 pcba-aid883 pcba-aid884 pcba-aid885 1562 32 589 1214 3391 163 69 734 73 870 106 656 8170 9676 12 904 other enzyme protein-protein interaction other enzyme other enzyme other enzyme other enzyme luciferase brca1-bach1 15hLO-2 CYP2C9 CYP3A4 CYP3A4\nMassively Multitask Networks for Drug Discovery", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
44
Dataset pcba-aid887 pcba-aid891 pcba-aid899 pcba-aid902* pcba-aid903* pcba-aid904* pcba-aid912 pcba-aid914 pcba-aid915 pcba-aid924* pcba-aid925 pcba-aid926 pcba-aid927* pcba-aid938 pcba-aid995* pcba-aid1030 pcba-aid1379* pcba-aid1452 pcba-aid1454* pcba-aid1457 pcba-aid1458 pcba-aid1460* pcba-aid1461 pcba-aid1468* pcba-aid1469 pcba-aid1471 pcba-aid1479 pcba-aid1631 pcba-aid1634 pcba-aid1688 Actives Inactives Target Class 1024 1548 1809 1872 338 528 445 218 436 1146 39 350 61 1775 699 15 963 562 177 536 722 5805 5662 2305 1039 169 288 788 892 154 2374 72 140 7836 7575 123 512 54 175 53 981 68 506 10 619 10 401 122 867 64 358 71 666 59 108 70 241 70 189 200 920 198 500 151 634 130 788 204 859 202 680 261 757 218 561 270 371 276 098 223 321
1502.02072#44
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 44, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Dataset pcba-aid887 pcba-aid891 pcba-aid899 pcba-aid902* pcba-aid903* pcba-aid904* pcba-aid912 pcba-aid914 pcba-aid915 pcba-aid924* pcba-aid925 pcba-aid926 pcba-aid927* pcba-aid938 pcba-aid995* pcba-aid1030 pcba-aid1379* pcba-aid1452 pcba-aid1454* pcba-aid1457 pcba-aid1458 pcba-aid1460* pcba-aid1461 pcba-aid1468* pcba-aid1469 pcba-aid1471 pcba-aid1479 pcba-aid1631 pcba-aid1634 pcba-aid1688 Actives Inactives Target Class 1024 1548 1809 1872 338 528 445 218 436 1146 39 350 61 1775 699 15 963 562 177 536 722 5805 5662 2305 1039 169 288 788 892 154 2374 72 140 7836 7575 123 512 54 175 53 981 68 506 10 619 10 401 122 867 64 358 71 666 59 108 70 241 70 189 200 920 198 500 151 634 130 788 204 859 202 680 261 757 218 561 270 371 276 098 223 321", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
45
401 122 867 64 358 71 666 59 108 70 241 70 189 200 920 198 500 151 634 130 788 204 859 202 680 261 757 218 561 270 371 276 098 223 321 275 479 262 774 263 512 218 200 path- path- Target 15hLO-1 CYP2D6 CYP2C19 H1299-p53A138V H1299-neo H1299-neo anthrax LF-PA internalization HIF-1 HIF-1 H1299-p53A138V EGFP-654 TSHR USP2a CNG ERK1/2 cascade ALDH1A1 luciferase 12hLO ERK1/2 cascade IMPase SMN2 K18 NPSR K18 TRb-SRC2 huntingtin TRb-SRC2 hPK-M2 hPK-M2 HTTQ103
1502.02072#45
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 45, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "401 122 867 64 358 71 666 59 108 70 241 70 189 200 920 198 500 151 634 130 788 204 859 202 680 261 757 218 561 270 371 276 098 223 321 275 479 262 774 263 512 218 200 path- path- Target 15hLO-1 CYP2D6 CYP2C19 H1299-p53A138V H1299-neo H1299-neo anthrax LF-PA internalization HIF-1 HIF-1 H1299-p53A138V EGFP-654 TSHR USP2a CNG ERK1/2 cascade ALDH1A1 luciferase 12hLO ERK1/2 cascade IMPase SMN2 K18 NPSR K18 TRb-SRC2 huntingtin TRb-SRC2 hPK-M2 hPK-M2 HTTQ103", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
46
other enzyme other enzyme other enzyme viability viability viability miscellaneous transcription fac- tor transcription fac- tor viability miscellaneous GPCR protease ion channel signalling way other enzyme other enzyme other enzyme signalling way other enzyme miscellaneous protein-protein interaction GPCR protein-protein interaction protein-protein interaction protein-protein interaction miscellaneous other enzyme other enzyme protein-protein interaction other enzyme other enzyme other enzyme other enzyme other enzyme miscellaneous other enzyme other enzyme other enzyme transcription fac- tor other enzyme 1087 1159 285 3477 715 1069 2008 1136 660 10 550 291 649 301 145 321 268 223 441 198 459 268 500 285 737 344 762 347 283 293 509 # pcba-aid1721 pcba-aid2100* pcba-aid2101* pcba-aid2147 pcba-aid2242* pcba-aid2326 pcba-aid2451 pcba-aid2517 pcba-aid2528 pcba-aid2546 # LmPK alpha-glucosidase glucocerebrosidase JMJD2E alpha-glucosidase influenza A NS1 FBPA APE1 BLM VP16 # pcba-aid2549 1210 233 706 # RECQ1 Massively Multitask Networks for Drug Discovery
1502.02072#46
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 46, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "other enzyme other enzyme other enzyme viability viability viability miscellaneous transcription fac- tor transcription fac- tor viability miscellaneous GPCR protease ion channel signalling way other enzyme other enzyme other enzyme signalling way other enzyme miscellaneous protein-protein interaction GPCR protein-protein interaction protein-protein interaction protein-protein interaction miscellaneous other enzyme other enzyme protein-protein interaction other enzyme other enzyme other enzyme other enzyme other enzyme miscellaneous other enzyme other enzyme other enzyme transcription fac- tor other enzyme\n1087 1159 285 3477 715 1069 2008 1136 660 10 550\n291 649 301 145 321 268 223 441 198 459 268 500 285 737 344 762 347 283 293 509\n# pcba-aid1721 pcba-aid2100* pcba-aid2101* pcba-aid2147 pcba-aid2242* pcba-aid2326 pcba-aid2451 pcba-aid2517 pcba-aid2528 pcba-aid2546\n# LmPK alpha-glucosidase glucocerebrosidase JMJD2E alpha-glucosidase influenza A NS1 FBPA APE1 BLM VP16\n# pcba-aid2549\n1210\n233 706\n# RECQ1\nMassively Multitask Networks for Drug Discovery", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
47
Dataset pcba-aid2551 pcba-aid2662 pcba-aid2675 pcba-aid2676 pcba-aid463254* pcba-aid485281 pcba-aid485290 pcba-aid485294* pcba-aid485297 pcba-aid485313 pcba-aid485314 pcba-aid485341* pcba-aid485349 pcba-aid485353 pcba-aid485360 pcba-aid485364 pcba-aid485367 pcba-aid492947 pcba-aid493208 pcba-aid504327 pcba-aid504332 pcba-aid504333 pcba-aid504339 pcba-aid504444 pcba-aid504466 pcba-aid504467 pcba-aid504706 pcba-aid504842 pcba-aid504845 pcba-aid504847 pcba-aid504891 pcba-aid540276* pcba-aid540317 Actives Inactives Target Class 16 666 110 99 1081 41 254 942 148 9126 7567 4491 1729 618 603 1485 10 700 557 80 342 759 30 586 15 670 16 857 7390 4169 7647 201 101 104 3515 34 4494 2126 288 772 293 953 279 333 361 124 330 640 341 253 343 503 362 056 311 481
1502.02072#47
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 47, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "Dataset pcba-aid2551 pcba-aid2662 pcba-aid2675 pcba-aid2676 pcba-aid463254* pcba-aid485281 pcba-aid485290 pcba-aid485294* pcba-aid485297 pcba-aid485313 pcba-aid485314 pcba-aid485341* pcba-aid485349 pcba-aid485353 pcba-aid485360 pcba-aid485364 pcba-aid485367 pcba-aid492947 pcba-aid493208 pcba-aid504327 pcba-aid504332 pcba-aid504333 pcba-aid504339 pcba-aid504444 pcba-aid504466 pcba-aid504467 pcba-aid504706 pcba-aid504842 pcba-aid504845 pcba-aid504847 pcba-aid504891 pcba-aid540276* pcba-aid540317 Actives Inactives Target Class 16 666 110 99 1081 41 254 942 148 9126 7567 4491 1729 618 603 1485 10 700 557 80 342 759 30 586 15 670 16 857 7390 4169 7647 201 101 104 3515 34 4494 2126 288 772 293 953 279 333 361 124 330 640 341 253 343 503 362 056 311 481", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
48
15 670 16 857 7390 4169 7647 201 101 104 3515 34 4494 2126 288 772 293 953 279 333 361 124 330 640 341 253 343 503 362 056 311 481 313 119 329 974 328 952 321 745 328 042 223 830 345 950 330 124 330 601 43 647 380 820 317 753 341 165 367 661 353 475 325 944 322 464 321 230 329 517 385 400 390 525 383 652 279 673 381 226 Target ROR gamma MLL-HOX-A MBNL1-CUG RXFP1 USP2a apoferritin TDP1 AmpC Rab9 NPC1 DNA polymerase beta AmpC ATM PLP L3MBTL1 TGR PFK beta2-AR mTOR GCN5L2 G9a BAZ2B JMJD2A Nrf2 HEK293T-ELG1-luc ELG1 p53 Mm-CPN RGS4 VDR Pin1 Marburg virus HP1-beta
1502.02072#48
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 48, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "15 670 16 857 7390 4169 7647 201 101 104 3515 34 4494 2126 288 772 293 953 279 333 361 124 330 640 341 253 343 503 362 056 311 481 313 119 329 974 328 952 321 745 328 042 223 830 345 950 330 124 330 601 43 647 380 820 317 753 341 165 367 661 353 475 325 944 322 464 321 230 329 517 385 400 390 525 383 652 279 673 381 226 Target ROR gamma MLL-HOX-A MBNL1-CUG RXFP1 USP2a apoferritin TDP1 AmpC Rab9 NPC1 DNA polymerase beta AmpC ATM PLP L3MBTL1 TGR PFK beta2-AR mTOR GCN5L2 G9a BAZ2B JMJD2A Nrf2 HEK293T-ELG1-luc ELG1 p53 Mm-CPN RGS4 VDR Pin1 Marburg virus HP1-beta", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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1502.02072
49
transcription fac- tor miscellaneous miscellaneous GPCR protease miscellaneous other enzyme other enzyme promoter promoter other enzyme other enzyme protein kinase protease protein-protein interaction other enzyme other enzyme GPCR protein kinase other enzyme other enzyme protein-protein interaction protein-protein interaction transcription fac- tor viability promoter miscellaneous other enzyme miscellaneous transcription fac- tor other enzyme miscellaneous protein-protein interaction other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme transcription fac- tor other enzyme other enzyme 25 034 3921 51 1987 3936 4715 1308 4894 335 826 382 731 386 206 393 298 382 117 383 994 384 951 398 438 pcba-aid588342* pcba-aid588453* pcba-aid588456* pcba-aid588579 pcba-aid588590 pcba-aid588591 pcba-aid588795 pcba-aid588855 luciferase TrxR1 TrxR1 DNA polymerase kappa DNA polymerase iota DNA polymerase eta FEN1 Smad3 # pcba-aid602179 pcba-aid602233 364 165 387 230 380 904 # IDH1 PGK Massively Multitask Networks for Drug Discovery
1502.02072#49
Massively Multitask Networks for Drug Discovery
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
http://arxiv.org/pdf/1502.02072
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande
stat.ML, cs.LG, cs.NE
Preliminary work. Under review by the International Conference on Machine Learning (ICML)
null
stat.ML
20150206
20150206
[]
{ "authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande", "chunk_id": 49, "doc_id": "1502.02072", "primary_category": "stat.ML", "published": 20150206, "source": "http://arxiv.org/pdf/1502.02072", "summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.", "text": "transcription fac- tor miscellaneous miscellaneous GPCR protease miscellaneous other enzyme other enzyme promoter promoter other enzyme other enzyme protein kinase protease protein-protein interaction other enzyme other enzyme GPCR protein kinase other enzyme other enzyme protein-protein interaction protein-protein interaction transcription fac- tor viability promoter miscellaneous other enzyme miscellaneous transcription fac- tor other enzyme miscellaneous protein-protein interaction other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme transcription fac- tor other enzyme other enzyme\n25 034 3921 51 1987 3936 4715 1308 4894\n335 826 382 731 386 206 393 298 382 117 383 994 384 951 398 438\npcba-aid588342* pcba-aid588453* pcba-aid588456* pcba-aid588579 pcba-aid588590 pcba-aid588591 pcba-aid588795 pcba-aid588855\nluciferase TrxR1 TrxR1 DNA polymerase kappa DNA polymerase iota DNA polymerase eta FEN1 Smad3\n# pcba-aid602179 pcba-aid602233\n364 165\n387 230 380 904\n# IDH1 PGK\nMassively Multitask Networks for Drug Discovery", "title": "Massively Multitask Networks for Drug Discovery", "year": 2015 }
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