2025/07/13 by Zhonglin Cao, Cao, Zhonglin, Mario Geiger +15 · 2 citations
Decision Sciences · Engineering · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning (cs.LG) #Microfluidic and Capillary Electrophoresis Applications #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2507.09785
openalex publication_date 2025/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.