Two subsets of molecules from ChEMBL-34[1].

Those molecular datasets might be useful to people training molecular generators.

After decompression, you will get:
chembl34_stable_ES_OA_LL.smi: 585,272 molecules.
chembl34_stable_ES_OA_DL.smi: 756,420 molecules.

stable=non-reactive molecules (filtered-out reactive functional groups from [5]).
https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_stable.py

ES=Easy Synthesis (SAscore <= 3.0) [2].
https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_SA.py

OA=Orally Available (according to a classifier trained on the dataset from [6]).

LL=Lead-Like (almost the definition from [3]).
https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_lead.py

DL=Drug-Like (definition from [4]).
https://github.com/UnixJunkie/molenc/blob/master/bin/molenc_drug.py

Bibliography

1. Zdrazil, B., Felix, E., Hunter, F., Manners, E. J., Blackshaw, J., Corbett, S., ... & Leach, A. R. (2024). The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic acids research, 52(D1), D1180-D1192. https://doi.org/10.1093/nar/gkad1004

2. Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1, 1-11. https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-1-8

3. Hann, M. M., & Oprea, T. I. (2004). Pursuing the leadlikeness concept in pharmaceutical research. Current opinion in chemical biology, 8(3), 255-263. https://doi.org/10.1016/j.cbpa.2004.04.003

4. Tran-Nguyen, V. K., Jacquemard, C., & Rognan, D. (2020). LIT-PCBA: an unbiased data set for machine learning and virtual screening. Journal of chemical information and modeling, 60(9), 4263-4273. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155

5. Lisurek, M., Rupp, B., Wichard, J., Neuenschwander, M., von Kries, J. P., Frank, R., ... & Kühne, R. (2010) Design of chemical libraries with potentially bioactive molecules applying a maximum common substructure concept. Molecular diversity, 14, 401-408. https://link.springer.com/article/10.1007/s11030-009-9187-z

6. Falcon-Cano, G., Molina, C., & Cabrera-Perez, M. A. (2020). ADME prediction with KNIME: development and validation of a publicly available workflow for the prediction of human oral bioavailability. Journal of chemical information and modeling, 60(6), 2660-2667. https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019