LIT-PCBA
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Selected PubChem Bioassays
| AID | Set | Target | Ligands | Actives | Inactives | PDB templates |
|---|---|---|---|---|---|---|
| 492947 | ADRB2 | Beta2 adrenergic receptor | Agonists | 17 | 311748 | 8 |
| 1030 | ALDH1 | Aldehyde dehydrogenase 1 | Inhibitors | 5363 | 101874 | 8 |
| 743075 | ESR_ago | Estrogen receptor α | Agonists | 13 | 4378 | 15 |
| 743080 | ESR_antago | Estrogen receptor α | Antagonists | 88 | 3820 | 15 |
| 588795 | FEN1 | FLAP Endonuclease | Inhibitors | 360 | 350718 | 1 |
| 2101 | GBA | Glucocerebrosidrase | Inhibitors | 163 | 291241 | 6 |
| 602179 | IDH1 | Isocitrate dihydrogenase | Inhibitors | 39 | 358757 | 14 |
| 504327 | KAT2A | Histone acetyltransferase KAT2A | Inhibitors | 194 | 342729 | 3 |
| 995 | MAPK1 | Mitogen-activated protein kinase 1 | Inhibitors | 308 | 61567 | 15 |
| 493208 | MTORC1 | Mechanistic target of rapamycin | Inhibitors | 97 | 32972 | 11 |
| 1777 | OPRK1 | Kappa opioid receptor | Agonists | 24 | 269475 | 1 |
| 1631 | PKM2 | Pyruvate kinase muscle isoform 2 | Inhibitors | 546 | 244679 | 9 |
| 743094 | PPARG | Peroxisome proliferator-activated receptor γ | Inhibitors | 24 | 4071 | 15 |
| 651631 | TP53 | Cellular tumor antigen p53 | Inhibitors | 64 | 3345 | 6 |
| 504847 | VDR | Vitamin D receptor | Inhibitors | 655 | 262648 | 2 |
Data Content
For each PubChem target set, the following ready-to-use input files are provided:
- PBDid_protein.mol2 (protein x-ray structure, MOL2 file format)
- PDBid_ligand.mol2 (ligand x-ray structure, MOL2 file format)
- active.smi (true active compounds: SMILES string, PubChem SID)
- inactive.smi (true inactive compounds: SMILES string, PubChem SID)
- active_T.smi (active compounds for training: SMILES string, PubChem SID)
- active_V.smi (active compounds for validation: SMILES string, PubChem SID)
- inactive_T.smi (inactive compounds for training: SMILES string, PubChem SID)
- inactive_V.smi (inactive compounds for validation: SMILES string, PubChem SID)
Please cite
V. Tran-Nguyen and D. Rognan LIT-PCBA: An unbiased data set for machine learning and virtual screening J. Chem. Inf. Model. 2020, 60, 9, 4263–4273