Skill UI

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.

RDKit is a comprehensive cheminformatics library providing robust Python APIs for molecular structure manipulation and analysis. It supports tasks ranging from molecular I/O (SMILES, SDF) and calculating physical descriptors (MW, LogP) to advanced substructure searching, 3D coordinate generation, and chemical reaction modeling. It is an essential toolkit for drug discovery, computational chemistry, and academic cheminformatics research.

Guide to building and troubleshooting TorchDrug 0.2.1 workflows for molecular graphs, property prediction, pretraining, generation, retrosynthesis, protein learning, and knowledge graph reasoning.