Results for “molecular-generation”

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

Applies physical principles to model biological systems, including protein folding, membrane transport, molecular forces, and neural signaling.

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

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.