用旋转平均流匹配加速分子构象生成，训练更快、推理更省。
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
- 引入SO(3)平均流目标，提升训练收敛速度与生成质量。
- 结合重流与蒸馏，实现少步甚至单步高质量构象生成。
- 适合需要快速生成分子3D结构的药物研发场景。
高效准确的分子构象生成对计算化学和药物发现至关重要。当前基于扩散或流模型的构象生成在训练和采样上需大量计算资源。本文基于流匹配，提出两种加速方法：一是采用SO(3)-Averaged Flow训练目标，相比条件最优传输流或Kabsch对齐流，收敛更快、生成质量更高；二是利用重流与蒸馏技术，实现仅需少数步骤甚至一步即可生成高质量分子构象。实验表明，该方法可达到当前最优的构象生成性能，为流模型在分子构象生成中的高效应用提供了可行路径。
原文摘要 · Abstract (English)
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.
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