Overview of TDC Datasets

At its core, TDC collects ML tasks and associated datasets across therapeutic modalities and stages of discovery. These tasks and datasets have the following properties:

Instrumenting disease treatment from bench to bedside with AI/ML: TDC covers a variety of learning tasks going from wet-lab target identification to biomedical product manufacturing.
Building off the latest biotechnological platforms: TDC is regularly updated with novel datasets and tasks, such as antibody therapeutics and gene editing.
Providing AI/ML-ready datasets: TDC datasets provide rich information on biomedical entities. This information is carefully curated, processed, and readily available in TDC.

Machine Learning Tasks in TDC

ML tasks cover a range of therapeutic modalities, including small molecules and biologics, including antibodies, peptides, miRNAs, and gene editing therapies. They also map to drug discovery and development pipelines:

Target discovery: Tasks to identify candidate drug targets.
Activity modeling: Tasks to screen and generate individual or combinatorial candidates with high binding activity towards targets.
Efficacy and safety: Tasks to optimize therapeutic signatures indicative of drug safety and efficacy.
Manufacturing: Tasks in support of synthesis and manufacturing of therapeutics.

*ML Tasks*	Small Molecules	Target Discovery
*ML Tasks*	Therapeutic Modalities	Stages of Discovery and Development
`ADME`
`Tox`
`HTS`
`QM`
`Yields`
`Epitope`
`Develop`
`CRISPROutcome`
`DTI`
`DDI`
`PPI`
`GDA`
`DrugRes`
`DrugSyn`
`PeptideMHC`
`AntibodyAff`
`MTI`
`Catalyst`
`TrialOutcome`
`MolGen`
`RetroSyn`
`Reaction`
More... find a comprehensive list of tasks in each ML Problem's page.

TDC-2 also introduced a variety of new data sources under the Resource Module. Information can be found on the tutorials in the github repo. Examples include CELLXGENE and PrimeKG.

Explore TDC Datasets

Single-instance Prediction

Multi-instance Prediction

Generation