mlsanityData to 7 Oct 2026

A Generative Model for Molecular Distance Geometry

arXiv 1909.11459 ↗First posted 25 Sep 2019stat.ML cs.LGcited by 73

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Abstract (arXiv)

Great computational effort is invested in generating equilibrium states for molecular systems using, for example, Markov chain Monte Carlo. We present a probabilistic model that generates statistically independent samples for molecules from their graph representations. Our model learns a low-dimensional manifold that preserves the geometry of local atomic neighborhoods through a principled learning representation that is based on Euclidean distance geometry. In a new benchmark for molecular conformation generation, we show experimentally that our generative model achieves state-of-the-art accuracy. Finally, we show how to use our model as a proposal distribution in an importance sampling scheme to compute molecular properties.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2019.

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Citing papers per year: 2021: 13; 2022: 12; 2023: 22; 2024: 15; 2025: 8; 2026: 3.

73 papers.

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  1. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
  2. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  3. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  4. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  5. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  6. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  7. 2022Diffusion-based Molecule Generation with Informative Prior Bridgescited by 104
  8. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  9. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  10. 2021SE(3) Equivariant Graph Neural Networks with Complete Local Framescited by 75
  11. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  12. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
38 more
  1. 2023Swallowing the Bitter Pill: Simplified Scalable Conformer Generationcited by 55
  2. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  3. 2022Unified 2D and 3D Pre-Training of Molecular Representationscited by 45
  4. 2024ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generationcited by 41
  5. 2023Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltoniancited by 38
  6. 20233D molecule generation by denoising voxel gridscited by 38
  7. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  8. 20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
  9. 2021Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphscited by 31
  10. 2022Direct Molecular Conformation Generationcited by 30
  11. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  12. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  13. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 25
  14. 2022Graph Neural Networks for Moleculescited by 21
  15. 2021Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learningcited by 19
  16. 2022MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencodercited by 16
  17. 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicscited by 14
  18. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  19. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  20. 2024Weisfeiler Leman for Euclidean Equivariant Machine Learningcited by 10
  21. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  22. 2024Molecular relaxation by reverse diffusion with time step predictioncited by 9
  23. 2021Auto-Encoding Molecular Conformationscited by 8
  24. 2023Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?cited by 8
  25. 2021Structure-aware generation of drug-like moleculescited by 7
  26. 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
  27. 2023CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generationcited by 7
  28. 2023CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learningcited by 5
  29. 2023Von Mises Mixture Distributions for Molecular Conformation Generationcited by 5
  30. 2024Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generationcited by 5
  31. 2025Sampling 3D Molecular Conformers with Diffusion Transformerscited by 5
  32. 2023Gradual Optimization Learning for Conformational Energy Minimizationcited by 4
  33. 2024SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learningcited by 4
  34. 2025Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflowcited by 4
  35. 2023Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusioncited by 3
  36. 2024Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learningcited by 3
  37. 2024EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Predictioncited by 3
  38. 2026Geometric Flow Matching for Molecular Conformation Generation via Manifold Decompositioncited by 3

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