Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Yuyan Ni, Shikun Feng, Haohan Chi, Bowen Zheng, Huan-ang Gao, Wei-Ying Ma, Zhi-Ming Ma, Yanyan Lan
Abstract
Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem, by formulating the diffusion process to follow a linear trajectory. The proposed process aligns well with the noise sensitivity characteristic of molecular structures and uniformly distributes reconstruction effort across the generative process, thus enhancing learning efficiency and efficacy. Consequently, SLDM achieves state-of-the-art performance on 3D molecule generation benchmarks, delivering a 100-fold improvement in sampling efficiency.
BibTeX
@inproceedings{
ni2025straightline,
title={Straight-Line Diffusion Model for Efficient 3D Molecular Generation},
author={Yuyan Ni and Shikun Feng and Haohan Chi and Bowen Zheng and Huan-ang Gao and Wei-Ying Ma and Zhi-Ming Ma and Yanyan Lan},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=waHF2ekuf2}
}