pytdc

AI-ready datasets for drug discovery tasks like ADME and toxicity are provided as curated benchmarks. You can load them directly via the TDC API to train and evaluate models on standardized single-instance and multi-instance tasks.

Scaffold splits are applied to test molecular generation model generalization by structurally separating training and test sets. You can generate these splits using the built-in TDC utilities to ensure robust evaluation across novel chemical structures.

Yes, you can benchmark retrosynthesis and molecular generation models using integrated oracles. These generation tools enable goal-directed design, distribution analysis, and reproducible experiments within the therapeutics data ecosystem.

This drug discovery benchmarking solution requires specific Python dependencies including numpy, pandas, scipy, and the tdc library. These packages handle data manipulation, scientific computing, and provide the core therapeutics data commons ecosystem.

The best way to evaluate drug-target interaction models on standardized datasets is using the provided multi-instance benchmarks. They offer comprehensive metadata and benchmark group support to compare model performance against established baselines.