mlsanityData to 7 Oct 2026

Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

arXiv 1906.00957 ↗First posted 2 Jun 2019stat.ML cs.LG physics.chem-phcited by 171

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Cited by 51 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells.

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2019.

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Citing papers per year: 2021: 13; 2022: 26; 2023: 37; 2024: 31; 2025: 43; 2026: 21.

171 papers.

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  1. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
  2. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  3. 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 268
  4. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  5. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
  6. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 176
  7. 2021E(n) Equivariant Normalizing Flowscited by 165
  8. 2022A 3D Generative Model for Structure-Based Drug Designcited by 148
  9. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  10. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  11. 2022Diffusion-based Molecule Generation with Informative Prior Bridgescited by 104
  12. 2023MatterGen: a generative model for inorganic materials designcited by 98
38 more
  1. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 96
  2. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  3. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 88
  4. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  5. 2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 82
  6. 2022Conditional Antibody Design as 3D Equivariant Graph Translationcited by 75
  7. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  8. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  9. 2024Space Group Constrained Crystal Generationcited by 71
  10. 2021Keeping it Simple: Language Models can learn Complex Molecular Distributionscited by 68
  11. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  12. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
  13. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
  14. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  15. 2023Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB filescited by 59
  16. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 59
  17. 2022MDM: Molecular Diffusion Model for 3D Molecule Generationcited by 58
  18. 2024Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generationcited by 53
  19. 2023Graph Generation with Diffusion Mixturecited by 52
  20. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  21. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 45
  22. 2024Transferable Boltzmann Generatorscited by 43
  23. 2023MUDiff: Unified Diffusion for Complete Molecule Generationcited by 41
  24. 2023Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Spacecited by 40
  25. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 40
  26. 20233D molecule generation by denoising voxel gridscited by 38
  27. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
  28. 2023Geometry-Complete Diffusion for 3D Molecule Generation and Optimizationcited by 36
  29. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  30. 2023Fractional Denoising for 3D Molecular Pre-trainingcited by 33
  31. 2022Direct Molecular Conformation Generationcited by 30
  32. 2022Diffeomorphic Counterfactuals with Generative Modelscited by 29
  33. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  34. 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 29
  35. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  36. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  37. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  38. 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25

50 most cited of 171 All 171 (JSON)