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CCC1=[O+][Cu-3]2([O+]=C(CC)C1)[O+]=C(CC)CC(CC)=[O+]2
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C(=Cc1ccccc1)C1=[O+][Cu-3]2([O+]=C(C=Cc3ccccc3)CC(c3ccccc3)=[O+]2)[O+]=C(c2ccccc2)C1
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CC(=O)N1c2ccccc2Sc2c1ccc1ccccc21
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CCOP(=O)(Nc1cccc(Cl)c1)OCC
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CC1=C2C(=COC(C)C2C)C(O)=C(C(=O)O)C1=O
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O=[N+]([O-])c1ccc(SSc2ccc([N+](=O)[O-])cc2[N+](=O)[O-])c([N+](=O)[O-])c1
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O=[N+]([O-])c1ccccc1SSc1ccccc1[N+](=O)[O-]
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CCCCCCCCCCCC(=O)Nc1ccc(SSc2ccc(NC(=O)CCCCCCCCCCC)cc2)cc1
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Sc1cccc2c(S)cccc12
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CCOP(N)(=O)c1ccccc1
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c1ccc2c(c1)Sc1ccccc1S2
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CC(C)CCS(=O)(=O)O
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CCCNP(=S)(NCCC)NCCC
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CCCCCCCCCCCCNP(=S)(NCCCCCCCCCCCC)NCCCCCCCCCCCC
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c1ccc2nsnc2c1
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[N-]=[N+]=CC(=O)OCC(N)C(=O)O
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c1ccn2nnnc2c1
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c1ccn2nncc2c1
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CCCCCOC(=S)S
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CC(C)C(C(=O)O)C(=O)O
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CCOC(=O)C(=O)C1CCCCC1=O
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CCOC(=O)CNS(=O)(=O)c1ccccc1
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CCN(CC)C(C)(O)CN
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Cc1cccc2c(=O)c3ccccc3oc12
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CCCCCCCCCCCC(=O)OCCOCCOCCOCCOCCOCCOCCOCCOCCO
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C1CN[Co-4]23(N1)(NCCN2)NCCN3
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CC(C)OC(=S)SSC(=S)OC(C)C
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CCN(CC)C(=O)N1CCN(C)CC1
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CC(=O)OC1COC(c2ccccc2)OC1C1OC(c2ccccc2)OCC1OC(C)=O
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Oc1ncnc2[nH]ncc12
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O=C1O[Cu-5]2(O)(O)(OC1=O)OC(=O)C(=O)O2
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O=Nc1ccc(O)c(N=O)c1O
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Oc1ccc(Nc2ccccc2)cc1
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CCCCCCc1ccc(O)cc1O
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CCCCCCCC[N+]12CN3CN(CN(C3)C1)C2
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CC(C)(O)O.CC1(O)C(O)C(O)C1(O)CO
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OC1COCOC1C(O)C1OCOCC1O
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CN(C)C(=S)SSC(=S)N(C)C
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O=[N+]([O-])c1ccc(C=Cc2ccc([N+](=O)[O-])cc2S(=O)(=O)O)c(S(=O)(=O)O)c1
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CCc1cc[n+]([Mn](SC#N)(SC#N)([n+]2ccc(CC)cc2)([n+]2ccc(CC)cc2)[n+]2ccc(CC)cc2)cc1
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N=c1[nH][nH]c(=N)[nH]1
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O=S(=O)(O)CCO
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O=C1CSC(=S)N1
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C1CCNCC1.S=C(S)N1CCCCC1
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C1SCSCS1
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CCC(CC)(C(=O)O)C(=O)O
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N#CC(=Cc1ccccc1)c1ccccc1
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N#CNC(=N)N
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O=C1C(O)=C(CCCC2CCC3CCCCC3C2)C(=O)c2ccccc21
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O=[N+]([O-])c1cc([As](=O)(O)O)ccc1O
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O=C(O)c1ccccc1S
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MoleculeNet HIV

HIV dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.

The task is to predict ability of molecules to inhibit HIV replication.

Characteristic Description
Tasks 1
Task type classification
Total samples 41127
Recommended split scaffold
Recommended metric AUROC

References

[1] AIDS Antiviral Screen Data https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data

[2] Wu, Zhenqin, et al. "MoleculeNet: a benchmark for molecular machine learning." Chemical Science 9.2 (2018): 513-530 https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a

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