Spaces:
Runtime error
Runtime error
# dGPredictor | |
================================== | |
### Requirements: | |
1. Python 3.8.10 | |
2. RDkit (http://www.rdkit.org/) | |
3. pandas (https://pandas.pydata.org/) | |
4. matplotlib (https://matplotlib.org/stable/users/installing.html) | |
5. Scikit-learn (https://scikit-learn.org/stable/) | |
6. Streamlit (https://streamlit.io/) | |
7. Openbabel (https://anaconda.org/openbabel/openbabel) | |
8. ChemAxon's Marvin >= 5.11 | |
9. Pulp | |
Installation | |
1. Python 3.8.10 (https://www.python.org/downloads/windows/) | |
Recommended- | |
- Create anaconda environment using command "conda create -n dGPredictor python=3.8 ipython" | |
- activate the env using command "conda activate dGPredictor" or "source activate dGPredictor" | |
2. RDkit | |
- type command "conda install -c conda-forge rdkit" in your dGPredictor env to install rdkit | |
3. Pandas | |
- "conda install pandas" | |
4. matplotlib | |
- "conda install -c conda-forge matplotlib" | |
5. Scikit-learn | |
- use command "pip install -U scikit-learn" | |
6. Streamlit | |
- use command "pip install -U streamlit" | |
7. Openbabel | |
- run "conda install -c conda-forge openbabel" | |
8. ChemAxon's Marvin (PkA value estimation) | |
- Marvin is only required for adding structures of novel metabolites/compounds that are not in the KEGG database | |
- instructions (https://chemaxon.com/products/marvin/download) | |
- add "cxcalc.bat (macOS) /cxcalc.exe (Windows)" to PATH and also in "./CC/chemaxon.py" file | |
- you will need to get a license to use ChemAxon (it is free for academic use) | |
9. Pulp | |
- use command "pip install -U pulp" | |
================================== | |
### Running web-interface locally using streamlit | |
- Model generation: Run "model_gen.py" using "python model_gen.py" once to create dGPredictor model file :- (Running this might take some time) | |
- run "streamlit run ./streamlit/main.py" from dGPredictor folder | |
- running KEGG reaction (doesn't require ChemAxon's Marvin) : copy paste the reaction equation into reaction section and click search | |
### Gibbs free energy prediction use automated group decomposition method | |
- Step 1: decompose the metabolites based on smiles files (see function decompse_ac in decompose_groups.py or notebook ) | |
- Step 2: create group changes vectors (i.e. reaction rules) based on group changes in metabolites of reactions (see get_rxn_rule in decompose_groups.py) | |
- Step 3: linear regression, Ridge Regression and Bayesian Ridge Regression in "predict.py" | |
- Step 4: Multiple regression models in notebook "analysis_dGPredictor.ipynb" | |
### Pathway design using novoStoic | |
- Run "mini_novoStoic.py" to see an example to design pathways for Isobutanol synthesis | |
# demo | |
![dGPredictor Demo](figures/dg_demo_py3.gif) | |