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.

Molecular conformer generation is a critical task in small molecule drug-discovery pipeline. At current stage, generative models for conformer generation are too slow to be applied in industrial-scale use cases. The motivation of our work is to accelerate flow-based generative model for molecular conformer generation, while maintaining high generation quality. We firstly propose a method to training flow-based generative models, called SO(3)-Averaged Flow, to improve generation quality and accelerate training. We also proposed to adopt the reflow and distillation algorithms to effectively enable one-step generation of molecular conformers while maintaining high generation quality. Combining these methods, our models show promising performance in efficient conformer generation, making them more practically useful in drug-discovery applications.