Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow

• URL: http://arxiv.org/abs/2507.09785v1
• Date: Sun, 13 Jul 2025 20:48:21 GMT
• Title: Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
• Authors: Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Kucukbenli,
• Abstract summary: We propose two mechanisms for accelerating training and inference of generative models for 3D molecular conformer generation.<n>For fast inference, we show that the reflow and distillation methods of flow-based models enable few-steps or even one-step molecular conformer generation with high quality.
• Score: 20.687237892960038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fast and accurate generation of molecular conformers is desired for downstream computational chemistry and drug discovery tasks. Currently, training and sampling state-of-the-art diffusion or flow-based models for conformer generation require significant computational resources. In this work, we build upon flow-matching and propose two mechanisms for accelerating training and inference of generative models for 3D molecular conformer generation. For fast training, we introduce the SO(3)-Averaged Flow training objective, which leads to faster convergence to better generation quality compared to conditional optimal transport flow or Kabsch-aligned flow. We demonstrate that models trained using SO(3)-Averaged Flow can reach state-of-the-art conformer generation quality. For fast inference, we show that the reflow and distillation methods of flow-based models enable few-steps or even one-step molecular conformer generation with high quality. The training techniques proposed in this work show a path towards highly efficient molecular conformer generation with flow-based models.

Related papers

• Align Your Flow: Scaling Continuous-Time Flow Map Distillation [63.927438959502226]
  Flow maps connect any two noise levels in a single step and remain effective across all step counts.<n>We extensively validate our flow map models, called Align Your Flow, on challenging image generation benchmarks.<n>We show text-to-image flow map models that outperform all existing non-adversarially trained few-step samplers in text-conditioned synthesis.
  arXiv  Detail & Related papers  (2025-06-17T15:06:07Z)
• Mean Flows for One-step Generative Modeling [64.4997821467102]
  We propose a principled and effective framework for one-step generative modeling.<n>A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training.<n>Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning.
  arXiv  Detail & Related papers  (2025-05-19T17:59:42Z)
• Straight-Line Diffusion Model for Efficient 3D Molecular Generation [25.63489191042975]
  We introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem.<n>The proposed process aligns well with the noise sensitivity characteristic of molecular structures and uniformly distributes reconstruction effort across the generative process.<n>SLDM achieves state-of-the-art performance on 3D molecule generation benchmarks, delivering a 100-fold improvement in sampling efficiency.
  arXiv  Detail & Related papers  (2025-03-04T13:23:58Z)
• Improving Molecular Graph Generation with Flow Matching and Optimal Transport [8.2504828891983]
  GGFlow is a discrete flow matching generative model incorporating optimal transport for molecular graphs. It incorporates an edge-augmented graph transformer to enable the direct communications among chemical bounds. GGFlow demonstrates superior performance on both unconditional and conditional molecule generation tasks.
  arXiv  Detail & Related papers  (2024-11-08T16:27:27Z)
• Conditional Synthesis of 3D Molecules with Time Correction Sampler [58.0834973489875]
  Time-Aware Conditional Synthesis (TACS) is a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties.
  arXiv  Detail & Related papers  (2024-11-01T12:59:25Z)
• Flow Generator Matching [35.371071097381346]
  Flow Generator Matching (FGM) is designed to accelerate the sampling of flow-matching models into a one-step generation. On the CIFAR10 unconditional generation benchmark, our one-step FGM model achieves a new record Fr'echet Inception Distance (FID) score of 3.08. MM-DiT-FGM one-step text-to-image model demonstrates outstanding industry-level performance.
  arXiv  Detail & Related papers  (2024-10-25T05:41:28Z)
• FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner [70.90505084288057]
  Flow-based models tend to produce a straighter sampling trajectory during the sampling process. We introduce several techniques including a pseudo corrector and sample-aware compilation to further reduce inference time. FlowTurbo reaches an FID of 2.12 on ImageNet with 100 (ms / img) and FID of 3.93 with 38 (ms / img)
  arXiv  Detail & Related papers  (2024-09-26T17:59:51Z)
• SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching [43.56824843205882]
  Semla is a scalable E(3)-equivariant message passing architecture.<n>SemlaFlow is trained to generate a joint distribution over atom types, coordinates, bond types and formal charges.<n>Our model produces state-of-the-art results on benchmark datasets with as few as 20 sampling steps.
  arXiv  Detail & Related papers  (2024-06-11T13:51:51Z)
• Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation [0.0]
  Flow matching is a recently proposed generative modeling framework that generalizes diffusion models. We extend the flow matching framework to categorical data by constructing flows that are constrained to exist on a continuous representation of categorical data known as the probability simplex. We find that, in practice, a simpler approach that makes no accommodations for the categorical nature of the data yields equivalent or superior performance.
  arXiv  Detail & Related papers  (2024-04-30T17:37:21Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.