Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure, Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik

TL;DR
Therapeutics Data Commons (TDC) is a comprehensive platform that unifies datasets, tasks, and tools for machine learning in drug discovery, aiming to accelerate scientific progress and clinical applications.
Contribution
This paper introduces TDC, the first integrated platform providing datasets, evaluation strategies, and community resources for ML in therapeutics, facilitating systematic research and development.
Findings
Existing algorithms struggle with real dataset shifts.
Heterogeneous data modeling remains challenging.
Robust generalization to new data is limited.
Abstract
Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful learning tasks and careful curation of datasets. Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics. To date, TDC includes 66 AI-ready datasets spread across 22 learning tasks and spanning the discovery and development of safe and effective medicines. TDC also provides an ecosystem of tools and community resources, including 33 data functions and types of meaningful data splits, 23 strategies for systematic model evaluation, 17 molecule generation oracles, and 29 public leaderboards. All resources are integrated and accessible via an open Python library. We carry out extensive experiments…
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Taxonomy
TopicsComputational Drug Discovery Methods · Machine Learning in Materials Science · Innovative Microfluidic and Catalytic Techniques Innovation
