mims-harvard/TDC resource

Health v2 · maintenance only

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Adoption not part of the score

1276 stars · 222 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Therapeutics Data Commons (TDC) is an open-science initiative and Python library providing AI-ready datasets, machine learning tasks, and curated benchmarks for drug discovery and therapeutic science. It includes data processing tools, systematic evaluation strategies, data splits, and molecule generation oracles, integrated via the PyTDC library.

Use cases

• benchmark machine learning models for drug discovery
• download AI-ready datasets for molecular property prediction
• evaluate drug-target binding affinity prediction models
• find standard data splits for biomedical ML benchmarks
• generate molecules with oracles for molecular optimization
• compare model performance on therapeutic science leaderboards
• train models on single-cell and clinical datasets

When to choose

• you need curated, benchmarked datasets for drug discovery or therapeutics ML
• you want standardized evaluation and data splits for biomedical tasks
• you are researching which AI methods work best for therapeutic applications

When to avoid

• you need a general-purpose cheminformatics toolkit rather than ML datasets and benchmarks
• your domain is unrelated to biology, chemistry, or medicine
• you need production drug discovery software rather than research datasets

Facets

dataset · maturity active

machine-learning benchmarking data-science data-generation bioinformatics machine-learning healthcare chemistry python cross-platform drug-discovery therapeutics benchmarks cheminformatics biomedicine datasets molecule-generation leaderboards

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mims-harvard/TDC")