Results for “chemistry”

Filters and prioritizes compound libraries in drug discovery using drug-likeness rules, structural alerts, complexity metrics, and a query language.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

Provides guidance for using RDKit to read and write molecular structures, calculate descriptors, generate fingerprints, perform substructure searches, and handle chemical reactions.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.