mlsanityData to 14 Oct 2026

On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space

arXiv 2310.04915 ↗First posted 7 Oct 2023physics.comp-ph cs.AI cs.LGcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps. Till now, it remains unclear how to effectively accelerate this procedure explicitly in SE(3)-invariant space, which greatly hinders its wide application in the real world. In this paper, we systematically study the diffusion mechanism in SE(3)-invariant space via the lens of approximate errors induced by existing methods. Thereby, we develop more precise approximate in SE(3) in the context of projected differential equations. Theoretical analysis is further provided as well as empirical proof relating hyper-parameters with such errors. Altogether, we propose a novel acceleration scheme for generating molecular conformations in SE(3)-invariant space. Experimentally, our scheme can generate high-quality conformations with 50x--100x speedup compared to existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 30 of its 42 references that are in the map. First posted on arXiv 7 Oct 2023.

Builds on

30 of 42 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Generative Diffusion Models on Graphs: Methods and Applications6 Feb
  2. 2023SE(3) diffusion model with application to protein backbone generation5 Feb
  3. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation6 Mar

Other references · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  7. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  8. 2023Consistency Modelscited by 1,822
  9. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 938
  10. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  11. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  12. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 607
15 more
  1. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 571
  2. 2020Directional Message Passing for Molecular Graphscited by 561
  3. 20183D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Datacited by 354
  4. 2022Torsional Diffusion for Molecular Conformer Generationcited by 263
  5. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 243
  6. 2022Riemannian Score-Based Generative Modellingcited by 219
  7. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  8. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 107
  9. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 88
  10. 2020Symmetry-Aware Actor-Critic for 3D Molecular Designcited by 55
  11. 2020Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Propertiescited by 51
  12. 2022Direct Molecular Conformation Generationcited by 30
  13. 2019Generating valid Euclidean distance matricescited by 19
  14. 2023Learning to Decouple Complex Systemscited by 3
  15. 2023Molecular Conformation Generation via Shifting Scorescited by 2

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  3. 2026Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
  4. 2024Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models
  5. 2024F3low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
  6. 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generation
  7. 2024Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
  8. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations