mlsanityData to 13 Oct 2026

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

arXiv 1811.12823 ↗First posted 29 Nov 2018cs.LG cs.AI cs.DBcited by 271

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Cited by 52 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervised predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides a training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses.

Key facts. Cited by 271 papers in the map (data through 13 Oct 2026). First posted on arXiv 29 Nov 2018.

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Citing papers per year: 2021: 23; 2022: 38; 2023: 38; 2024: 50; 2025: 64; 2026: 58.

271 papers.

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  1. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 509
  2. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 301
  3. 2021Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Developmentcited by 260
  4. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 191
  5. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
  6. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 178
  7. 2022Translation between Molecules and Natural Languagecited by 175
  8. 2021Geometric Deep Learning on Molecular Representationscited by 113
  9. 2024Scientific Large Language Models: A Survey on Biological & Chemical Domainscited by 80
  10. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 77
  11. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  12. 2021Large-Scale Wasserstein Gradient Flowscited by 76
38 more
  1. 2022SELFIES and the future of molecular string representationscited by 76
  2. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 75
  3. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  4. 2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
  5. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  6. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  7. 2021High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learningcited by 54
  8. 2021Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networkscited by 53
  9. 2023Gotta be SAFE: A New Framework for Molecular Designcited by 45
  10. 2024Graph Diffusion Transformers for Multi-Conditional Molecular Generationcited by 44
  11. 2025El Agente: An Autonomous Agent for Quantum Chemistrycited by 44
  12. 2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
  13. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
  14. 2024PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusioncited by 37
  15. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
  16. 2022Group SELFIES: A Robust Fragment-Based Molecular String Representationcited by 32
  17. 2023Genetic algorithms are strong baselines for molecule generationcited by 31
  18. 2025KL-Regularized Reinforcement Learning is Designed to Mode Collapsecited by 31
  19. 2024Cometh: A continuous-time discrete-state graph diffusion modelcited by 30
  20. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 30
  21. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  22. 2021How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Moleculescited by 27
  23. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  24. 2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 27
  25. 2024DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimizationcited by 27
  26. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  27. 2023nach0: Multimodal Natural and Chemical Languages Foundation Modelcited by 26
  28. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  29. 2024Unifying Generation and Prediction on Graphs with Latent Graph Diffusioncited by 25
  30. 2024Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generationcited by 25
  31. 2021MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generationcited by 22
  32. 2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 22
  33. 2022Graph Neural Networks for Moleculescited by 21
  34. 2024L+M-24: Building a Dataset for Language + Molecules @ ACL 2024cited by 21
  35. 2021Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attentioncited by 20
  36. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  37. 2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19
  38. 2023De novo Drug Design using Reinforcement Learning with Multiple GPT Agentscited by 19

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