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[**Nature Chemical Biology Paper**](https://www.nature.com/articles/s41589-022-01131-2) | [**NeurIPS Paper**](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/4c56ff4ce4aaf9573aa5dff913df997a-Abstract-round1.html) | [**GitHub**](https://github.com/mims-harvard/TDC) | [**Leaderboards**](https://tdcommons.ai/benchmark/overview/) | [**Datasets**](https://tdcommons.ai/overview/) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/cloudposse.svg?style=social&label=Follow%20%40ProjectTDC)](https://twitter.com/ProjectTDC) Artificial intelligence is poised to enable breakthroughs and discoveries in therapeutic science. Therapeutics Data Commons is a global initiative to access and evaluate artificial intelligence capability across therapeutic modalities and stages of discovery. The Commons is a resource with AI-solvable tasks, AI-ready datasets, and curated benchmarks, providing an ecosystem of tools, libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles.
Therapeutics Commons website
  from tdc.single_pred import ADME
  data = ADME(name = 'HIA_Hou')
  # split into train/val/test with scaffold split methods
  split = data.get_split(method = 'scaffold')
  # get the entire data in the various formats
  data.get_data(format = 'df')
      
Retrieve AI tasks, data functions, model evaluators and benchmarks
Find all Therapeutics Commons models in the Hub

More information: Therapeutics Commons Slack Workspace, Release News