1. [Publications](/publications)
2. Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
 
 # Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 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.



 ## Authors



Zhonglin Cao (NVIDIA)

Mario Geiger (NVIDIA)

Allan Dos Santos Costa (NVIDIA, MIT Center for Bits and Atoms)

Danny Reidenbach (NVIDIA)

[Karsten Kreis](/person/karsten-kreis)

[Tomas Geffner](/person/tomas-geffner)

Franco Pellegrini (NVIDIA)

Guoqing Zhou (NVIDIA)

Emine Kucukbenli (NVIDIA)

 

 

 ## Publication Date



Sunday, July 13, 2025

 

 ## Published in



[International Conference on Machine Learning (ICML) 2025](https://arxiv.org/abs/2507.09785)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Generative AI](/research-area/generative-ai)