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ChEMBL is a manually curated bio‑active molecule database with drug‑like properties, integrating chemical, bio‑activity, and genomic data to facilitate the translation of genomic information into effective new drugs. The dataset contains 1,941,405 molecules, split into training, validation, and test sets for distribution‑learning and goal‑directed molecular generation tasks, i.e., generating new molecules with desired properties.

The dataset is derived from the Clean Leads subset of the ZINC database, filtered by molecular weight (250–350 Da), number of rotatable bonds (≤ 7), and XlogP (≤ 3.5). Molecules containing charged atoms or atoms other than C, N, S, O, F, Cl, Br, H, or rings larger than eight atoms were removed. The dataset also applies medicinal chemistry filters (MCFs) and PAINS filters. The final set comprises 1,936,962 molecular structures, split into a training set (~1.6 M molecules), a test set (~176 k molecules), and a scaffold test set (~176 k molecules). The scaffold test set contains unique Bemis‑Murcko scaffolds not present in the training or test sets, enabling evaluation of a model's ability to generate novel scaffolds.