domains / families / science / torchdrug

torchdrug

combined

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs. Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

When to use

• SMILES strings or molecular structures
• Protein sequences or 3D structures (PDB files)
• Chemical reactions and retrosynthesis
• Biomedical knowledge graphs
• Drug discovery datasets
• Predicting molecular properties (solubility, toxicity, activity)
• Protein function or structure prediction
• Drug-target binding prediction

Skill page

Conflict

The K-Dense-AI copy is a version-pinned, environment-aware procedure (TorchDrug 0.2.1, Python 3.7-3.10, PyTorch 1.8-2.0, specific wheel pins, ClinTox/Engine workflow, and explicit Apple Silicon/CPU constraints). The other two copies are near-identical, broader capability catalogs that use a generic installation command and a manual PyTorch training loop instead of the Engine, and they omit the version guard and dependency pinning. Because the K-Dense copy contains material steps the others lack (version guard, pinned installation, Engine-based training, dataset cache repair) and the others contain a broader capability overview and a manual training loop that the K-Dense copy does not emphasize, crowning one would hide a real alternative. The two non-K-Dense copies are effectively duplicates of each other.