mlsanityData to 7 Oct 2026

Diffusion-based Molecule Generation with Informative Prior Bridges

arXiv 2209.00865 ↗First posted 2 Sep 2022cs.LGcited by 104

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Cited by 61 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

32 of 55 references.

References · 32

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  6. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  7. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 670
  8. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
  9. 2021Diffusion Schr"odinger Bridge with Applications to Score-Based Generative Modelingcited by 594
  10. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  11. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  12. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
20 more
  1. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  2. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  3. 2020Learning Gradient Fields for Shape Generationcited by 245
  4. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  5. 2021Likelihood Training of Schr"odinger Bridge using Forward-Backward SDEs Theorycited by 216
  6. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
  7. 2021E(n) Equivariant Normalizing Flowscited by 165
  8. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  9. 2021Solving Schr"odinger Bridges via Maximum Likelihoodcited by 140
  10. 2021Deep Generative Learning via Schr"odinger Bridgecited by 103
  11. 2022Let us Build Bridges: Understanding and Extending Diffusion Generative Modelscited by 102
  12. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  13. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  14. 2019A Generative Model for Molecular Distance Geometrycited by 73
  15. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  16. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  17. 2022Equivariant Point Cloud Analysis via Learning Orientations for Message Passingcited by 32
  18. 2016Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Modelcited by 31
  19. 2022Score-Based Generative Models for Molecule Generationcited by 10
  20. 2022How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversitycited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 27; 2024: 35; 2025: 22; 2026: 15.

104 papers.

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  1. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  2. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  3. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
  4. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  5. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  6. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  7. 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learningcited by 145
  8. 2024Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Controlcited by 136
  9. 2023Diffusion Bridge Mixture Transports, Schr"odinger Bridge Problems and Generative Modelingcited by 80
  10. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  11. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimizationcited by 72
  12. 2023MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generationcited by 65
38 more
  1. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 59
  2. 2022Fast Point Cloud Generation with Straight Flowscited by 56
  3. 2023Graph Generation with Diffusion Mixturecited by 52
  4. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generationcited by 50
  5. 2024AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policiescited by 50
  6. 20233D molecule generation by denoising voxel gridscited by 38
  7. 2023Geometry-Complete Diffusion for 3D Molecule Generation and Optimizationcited by 36
  8. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  9. 2024SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flowcited by 30
  10. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  11. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  12. 2023Towards Symmetry-Aware Generation of Periodic Materialscited by 25
  13. 2024Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Samplingcited by 25
  14. 2025Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modelingcited by 25
  15. 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packingcited by 24
  16. 2024OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offscited by 22
  17. 2023Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processescited by 21
  18. 2024UniGEM: A Unified Approach to Generation and Property Prediction for Moleculescited by 20
  19. 2023DiffTAD: Temporal Action Detection with Proposal Denoising Diffusioncited by 19
  20. 2024Unified Generative Modeling of 3D Molecules via Bayesian Flow Networkscited by 18
  21. 2024Reward-Directed Score-Based Diffusion Models via q-Learningcited by 18
  22. 2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridgecited by 16
  23. 2023Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Actioncited by 15
  24. 2024A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Sciencecited by 14
  25. 2025CoDe: Blockwise Control for Denoising Diffusion Modelscited by 14
  26. 2023Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Predictioncited by 13
  27. 2024Geometric Representation Condition Improves Equivariant Molecule Generationcited by 13
  28. 2025Equivariant Neural Diffusion for Molecule Generationcited by 13
  29. 2024Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transportcited by 11
  30. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  31. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  32. 2023ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Modelscited by 9
  33. 2024Molecular relaxation by reverse diffusion with time step predictioncited by 9
  34. 2025Constrained Generative Modeling with Manually Bridged Diffusion Modelscited by 8
  35. 2025Straight-Line Diffusion Model for Efficient 3D Molecular Generationcited by 8
  36. 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
  37. 2024Decomposed Direct Preference Optimization for Structure-Based Drug Designcited by 7
  38. 2025Unified Guidance for Geometry-Conditioned Molecular Generationcited by 7

50 most cited of 104 All 104 (JSON)

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  1. 2022MDM: Molecular Diffusion Model for 3D Molecule Generation
  2. 2025D3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation
  3. 2026Autoregressive latent diffusion for 3D molecule generation
  4. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  5. 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
  6. 2024Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
  7. 2025Unraveling the Potential of Diffusion Models in Small Molecule Generation
  8. 2023Modeling Molecular Structures with Intrinsic Diffusion Models