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 | Therapeutic Modalities | Stages of Discovery and Development | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Small Molecules | MacroMolecules | Cell & Gene Therapy | Peptides | Target Discovery | Activity Modeling | Efficacy & Safety | Clinical Trial | Manufacturing | |
ADME |
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Tox |
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HTS |
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QM |
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Yields |
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Epitope |
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Develop |
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CRISPROutcome |
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DTI |
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DDI |
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PPI |
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GDA |
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DrugRes |
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DrugSyn |
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PeptideMHC |
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AntibodyAff |
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MTI |
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Catalyst |
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TrialOutcome |
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MolGen |
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RetroSyn |
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Reaction |
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MPC |
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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