pytdc

combined

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction. Provides Therapeutics Data Commons workflows through PyTDC for registry discovery, dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle scoring. Use when working with TDC therapeutic ML datasets or benchmarks.

When to use

Working with drug discovery or therapeutic ML datasets
Benchmarking machine learning models on standardized pharmaceutical tasks
Predicting molecular properties (ADME, toxicity, bioactivity)
Predicting drug-target or drug-drug interactions
Generating novel molecules with desired properties
Accessing curated datasets with proper train/test splits (scaffold, cold-split)
Using molecular oracles for property optimization
You need **curated, AI-ready datasets** for drug discovery tasks (e.g., ADME, toxicity, bioactivity, DTI/DDI).

Skill page

Conflict

The three copies cover the same PyTDC domain but diverge significantly in operational detail and safety constraints. The K-Dense-AI copy is a rigorous, version-pinned operational guide that emphasizes environment isolation (CPython 3.11, setuptools 80.9.0), network/storage planning, and metadata-only discovery before data loading. The aipoch and FreedomIntelligence copies are broader reference guides that list specific dataset names, task categories, and provide a complete end-to-end code example (load, split, train, evaluate, oracle), but they lack the strict version pinning and safety protocols found in the K-Dense-AI copy. Conversely, the K-Dense-AI copy lacks the specific dataset catalogs and the full training/evaluation code example provided by the other two. Combining them yields a skill that is both operationally safe (pinned env, network checks) and practically useful (specific datasets, code examples).