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

Zhonglin Cao; Mario Geiger; Allan dos Santos Costa; Danny Reidenbach; Karsten Kreis; Tomas Geffner; Franco Pellegrini; Guoqing Zhou; Emine Kucukbenli

arXiv · 2507.09785

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

Zhonglin Cao·Mario Geiger·Allan dos Santos Costa·Danny Reidenbach·Karsten Kreis·Tomas Geffner·Franco Pellegrini·Guoqing Zhou·Emine Kucukbenli

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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.

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Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Kucukbenli. 2025-07-13. Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. https://arxiv.org/abs/2507.09785

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