mlsanityData to 7 Oct 2026

MoleculeNet: A Benchmark for Molecular Machine Learning

arXiv 1703.00564 ↗First posted 2 Mar 2017cs.LG physics.chem-ph stat.MLcited by 1,037

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Cited by 35 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Key facts. Cited by 1,037 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2017.

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Citing papers per year: 2021: 102; 2022: 150; 2023: 190; 2024: 204; 2025: 211; 2026: 180.

1,037 papers.

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  6. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 258
  7. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  8. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  9. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  10. 2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 203
  11. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  12. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
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  6. 2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 165
  7. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
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  9. 20213D Infomax improves GNNs for Molecular Property Predictioncited by 146
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  13. 2023Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Modelscited by 131
  14. 2022A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Languagecited by 129
  15. 2023MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adaptercited by 128
  16. 2024LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Datasetcited by 124
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  19. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  20. 2021DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networkscited by 117
  21. 2021Geometric Deep Learning on Molecular Representationscited by 113
  22. 2021An Empirical Study of Graph Contrastive Learningcited by 113
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  26. 2023BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associationscited by 100
  27. 2021ProtGNN: Towards Self-Explaining Graph Neural Networkscited by 96
  28. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 96
  29. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  30. 2022How Powerful are K-hop Message Passing Graph Neural Networkscited by 90
  31. 2021Generative Models as Distributions of Functionscited by 89
  32. 2022Periodic Graph Transformers for Crystal Material Property Predictioncited by 87
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  34. 2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
  35. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  36. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  37. 2023GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Textcited by 82
  38. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81

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