Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

By Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf H. Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik. NeurIPS Datasets and Benchmarks, 2021

487 citations recorded by the source corpus.

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 on selected datasets, demonstrating that even the strongest algorithms fall short of solving key therapeutics challenges, including real dataset distributional shifts, multi-scale modeling of heterogeneous data, and robust generalization to novel data points. We envision that TDC can facilitate algorithmic and scientific advances and considerably accelerate machine-learning model development, validation and transition into biomedical and clinical implementation. TDC is an open-science initiative available at https://tdcommons.ai.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/54ca116f1e9a45768a3a2c47a4608ff34adefa0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Connor W. Coley
2. Pushmeet Kohli
3. Yue Zhao
4. Jure Leskovec
5. Jimeng Sun
6. Yoshua Bengio
7. Anima Anandkumar

In Computational Theory and Mathematics

1. Tianfan Fu
2. Jimeng Sun
3. Marinka Zitnik
4. Shengchao Liu
5. Jure Leskovec
6. Kexin Huang
7. Jian Tang
8. Yuanqi Du

In Materials Chemistry

1. Jure Leskovec
2. Jian Tang
3. Tianfan Fu
4. Jimeng Sun
5. Marinka Zitnik
6. Shengchao Liu
7. Yuanqi Du
8. Cao Xiao

Citations in the corpus

• Differentiable Scaffolding Tree for Molecular Optimization (2021, background): “[19] Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik.”
• Scientific discovery in the age of artificial intelligence (2023, background): “For instance, it is estimated that there are approximately 10 60 molecules, whereas even the largest chemical libraries contain fewer than 10 10 molecules 12,159 .”
• ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “Interpretability in machine learning is an important concept, as it provides domain experts some insights into how algorithms make their decisions ([52], [53], [54], [55], [56], [57]).”
• Building a knowledge graph to enable precision medicine (2022, background): “Collectively, these knowledge graphs have lent themselves to a variety of scientific discoveries 67,68 , methodological innovations 69–71 and biomedical benchmarking 31,35,72 .”
• ADBench: Anomaly Detection Benchmark (2022, background): “Also, future benchmarks can consider the latest algorithms [28, 99, 161], and curate datasets from emerging fields like drug discovery [69], molecule optimization [49, 50], interpretability and explainability [123, 180], and bias and fairness [32, 67, 123, 159, 165, 190].”