Results for “molecular-machine-learning”
6 skills
lingxling pytdc
Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.
253 · bundle
k-dense-ai deepchem
Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.
30.2k · bundle
More results
k-dense-ai torchdrug
Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.
30.2k · bundle
lingxling molfeat
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.
253 · bundle
k-dense-ai molecular-dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).
30.2k · bundle
k-dense-ai molfeat
Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.
30.2k · bundle