Aurigene-AI/MoleculeNet_HIV

Mirrored by Aurigene AI

Discovery stage: Hit generation

Compounds screened for inhibition of HIV replication. The classic large-scale virtual screening benchmark.

Rows: 41,127 (hiv.csv 41,127)

Pairs with Aurigene-AI/MoLFormer-XL-both-10pct from our model catalogue.

Upstream: scikit-fingerprints/MoleculeNet_HIV - all credit to the original authors and to the researchers who produced the underlying data; the dataset card and licence below are theirs.

Explore the rest of the catalogue: Molecule Explorer - Protein Target Explorer - Drug Discovery Model Hub

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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

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], 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

[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