scikit-fingerprints/MoleculeNet_HIV
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 Warning: in newer RDKit vesions, 7 molecules from the original dataset are not read correctly due to disallowed hypervalent… See the full description on the dataset page: https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_HIV.
MoleculeNet HIV
HIV dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict ability of molecules to inhibit HIV replication.
Warning: in newer RDKit vesions, 7 molecules from the original dataset are not read correctly due to disallowed hypervalent states of some atoms (see release notes). This version of the HIV dataset contains manual fixes for those molecules, made by cross-referencing original NCI data [[1]](#1), PubChem substructure search, and visualization with ChemAxon Marvin. In OGB scaffold split, used for benchmarking, first 2 of those problematic 7 are from the test set. Applied mapping is:
"O=C1O[Al]23(OC1=O)(OC(=O)C(=O)O2)OC(=O)C(=O)O3" -> "C1(=O)C(=O)O[Al-3]23(O1)(OC(=O)C(=O)O2)OC(=O)C(=O)O3"
"Cc1ccc([B-2]2(c3ccc(C)cc3)=NCCO2)cc1" -> "[B-]1(NCCO1)(C2=CC=C(C=C2)C)C3=CC=C(C=C3)C"
"Oc1ccc(C2Oc3cc(O)cc4c3C(=[O+][AlH3-3]35([O+]=C6c7c(cc(O)cc7[OH+]3)OC(c3ccc(O)cc3O)C6O)([O+]=C3c6c(cc(O)cc6[OH+]5)OC(c5ccc(O)cc5O)C3O)[OH+]4)C2O)c(O)c1" -> "C1[C@@H]([C@H](OC2=C1C(=CC(=C2C3=C(OC4=CC(=CC(=C4C3=O)O)O)C5=CC=C(C=C5)O)O)O)C6=CC=C(C=C6)O)O"
"CC1=C2[OH+][AlH3-3]34([O+]=C2C=CN1C)([O+]=C1C=CN(C)C(C)=C1[OH+]3)[O+]=C1C=CN(C)C(C)=C1[OH+]4" -> "CC1=C(C(=O)C=CN1C)[O-].CC1=C(C(=O)C=CN1C)[O-].CC1=C(C(=O)C=CN1C)[O-].[Al+3]"
"CC(c1cccs1)=[N+]1[N-]C(N)=[S+][AlH3-]12[OH+]B(c1ccccc1)[OH+]2" -> "B1(O[Al](O1)N(C(=S)N)/N=C(/C)\C2=CC=CS2)C3=CC=CC=C3"
"CC(c1ccccn1)=[N+]1[N-]C(N)=[S+][AlH3-]12[OH+]B(c1ccccc1)[OH+]2" -> "B1(O[Al](O1)N(C(=S)N)/N=C(/C)\C2=CC=CC=N2)C3=CC=CC=C3"
"[Na+].c1ccc([SH+][GeH2+]2[SH+]c3ccccc3[SH+]2)c([SH+][GeH2+]2[SH+]c3ccccc3[SH+]2)c1" -> "C1=CC=C(C(=C1)[SH2+])[SH2+].C1=CC=C(C(=C1)[SH2+])[SH2+].C1=CC=C(C(=C1)[SH2+])[SH2+].[Ge].[Ge]"References
<a id="1">[1]</a> AIDS Antiviral Screen Data https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data
<a id="2">[2]</a> 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