mlsanityData to 7 Oct 2026

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

arXiv 2102.09548 ↗First posted 18 Feb 2021cs.LG cs.CY q-bio.BMcited by 258

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Abstract (arXiv)

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.

Key facts. Cited by 258 papers in the map (data through 7 Oct 2026). Builds on 43 of its 182 references that are in the map. First posted on arXiv 18 Feb 2021.

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43 of 182 references.

References · 43

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31 more
  1. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  2. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  3. 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 294
  4. 2017Grammar Variational Autoencodercited by 283
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 28; 2023: 36; 2024: 56; 2025: 64; 2026: 71.

258 papers.

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  1. 2022ADBench: Anomaly Detection Benchmarkcited by 230
  2. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  3. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  4. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 99
  5. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 96
  6. 2022Evaluating Explainability for Graph Neural Networkscited by 85
  7. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  8. 2025Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Expertscited by 85
  9. 2022BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphscited by 80
  10. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  11. 2022PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understandingcited by 77
  12. 2025BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biologycited by 75
38 more
  1. 2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
  2. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
  3. 2021Differentiable Scaffolding Tree for Molecular Optimizationcited by 53
  4. 2025GenMol: A Drug Discovery Generalist with Discrete Diffusioncited by 47
  5. 2023Gotta be SAFE: A New Framework for Molecular Designcited by 45
  6. 2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
  7. 2023GNNDelete: A General Strategy for Unlearning in Graph Neural Networkscited by 43
  8. 2023Does Invariant Graph Learning via Environment Augmentation Learn Invariance?cited by 42
  9. 2024DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaborationcited by 40
  10. 2025TxGemma: Efficient and Agentic LLMs for Therapeuticscited by 40
  11. 2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 39
  12. 2023Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasetscited by 37
  13. 2024Genetic-guided GFlowNets for Sample Efficient Molecular Optimizationcited by 36
  14. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  15. 2022A Systematic Survey of Chemical Pre-trained Modelscited by 34
  16. 2024Are large language models superhuman chemists?cited by 34
  17. 2024Diffusion Models as Constrained Samplers for Optimization with Unknown Constraintscited by 33
  18. 2022PyGOD: A Python Library for Graph Outlier Detectioncited by 31
  19. 2024Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domainscited by 30
  20. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  21. 2022Bayesian Optimization with Conformal Prediction Setscited by 27
  22. 2024SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraintscited by 26
  23. 2024Tx-LLM: A Large Language Model for Therapeuticscited by 23
  24. 2025Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Modelscited by 23
  25. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  26. 2024Molecule Generation with Fragment Retrieval Augmentationcited by 22
  27. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  28. 2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19
  29. 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Modelcited by 19
  30. 2024textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learningcited by 19
  31. 2023Searching for High-Value Molecules Using Reinforcement Learning and Transformerscited by 18
  32. 2023Advancing Bayesian Optimization via Learning Correlated Latent Spacecited by 18
  33. 2024Generative Artificial Intelligence for Navigating Synthesizable Chemical Spacecited by 18
  34. 2023Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Designcited by 17
  35. 2022Deep learning for drug repurposing: methods, databases, and applicationscited by 16
  36. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 16
  37. 2024Adding Conditional Control to Diffusion Models with Reinforcement Learningcited by 16
  38. 2024Evaluating representation learning on the protein structure universecited by 16

50 most cited of 258 All 258 (JSON)