Overview of TDC Data Functions
TDC implements a comprehensive suite of functions frequently used in therapeutics ML. This functionality is wrapped in an easy-to-use interface.
Broadly, TDC functions cover the following four major categories:
- Model Evaluation: TDC implements a series of metrics and performance functions to debug ML models, evaluate model performance for any task in TDC, and assess whether model predictions generalize to out-of-distribution datasets.
- Dataset Splits: Therapeutic applications require ML models to generalize to out-of-distribution samples. TDC implements various data splits to reflect realistic learning settings.
- Data Processing: As therapeutics ML covers a wide range of data modalities and requires numerous repetitive processing functions, TDC implements wrappers and useful data helpers for them.
- Molecule Generation Oracles: Molecular design tasks require oracle functions to measure the quality of generated entities. TDC implements over 17 molecule generation oracles, representing the most comprehensive colleciton of molecule oracles. Each oracle is tailored to measure the quality of AI-generated molecules in a specific dimension.
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