pytdc

You can access curated datasets for drug discovery machine learning by loading over 200 benchmarks covering ADME, toxicity, and drug-target interactions directly through this Skill. It provides standardized data splits and evaluation metrics for rapid model training and testing.

Molecular toxicity and ADME prediction benchmarks are available as single-instance prediction datasets within the curated collection. You can load multiple toxicity datasets, train models using scaffold splits, and evaluate performance using standard metrics like ROC-AUC.

Scaffold splits for cheminformatics models are supported as predefined benchmark splitting strategies. You can load a dataset like Caco2_Wang and retrieve its scaffold split to ensure robust generalization during machine learning model training and evaluation.

Yes, you can benchmark molecular generation models using standardized evaluation metrics provided by this Skill. It facilitates machine learning model evaluation across molecular generation tasks alongside single-instance and multi-instance prediction benchmarks.

Drug-target interaction prediction datasets are fully supported as multi-instance prediction benchmarks. You can utilize these curated datasets for training and evaluating machine learning models focused on drug-target and drug-drug interactions.

The best way to evaluate drug discovery models across multiple datasets is using predefined benchmark groups with standardized data splitting strategies. This Skill supports consistent evaluation across ADME, toxicity, and interaction datasets with built-in metrics.