pytdc

To access AI-ready drug discovery datasets, you can load curated pharmacological data covering ADME properties, toxicity, and drug-target interactions directly for machine learning benchmarking. This provides standardized datasets ready for model evaluation.

The best way to split pharmaceutical datasets is by applying scaffold splitting strategies to ensure structurally diverse train-test partitions. This prevents data leakage and provides reliable evaluation metrics for chemoinformatics model training.

Yes, you can convert molecular formats and apply filters to drug-target interaction data using built-in data processing utilities. These functions prepare raw pharmacological data for standardized benchmarking and model evaluation tasks.

This data utility supports evaluation metrics for ADMET benchmarking tasks by providing standardized functions to compute model performance. It integrates dataset access with metric calculation to streamline end-to-end drug discovery workflows.

To prepare train-test splits for toxicity prediction models, use the available data splitting utilities to partition curated molecular datasets. This ensures appropriate separation of training and validation data for robust pharmacological model evaluation.