PFM: perturbed flow matching for structure-based drug design.

Authors

Yankai Yu, Guikun Xu, Zhuyang Xie, Yan Yang, Yongquan Jiang, Xiaobo Zhou, Kang Li

Published in

Bioinformatics (Oxford, England). Nov 10, 2026. Epub Nov 10, 2026.

Abstract

Target-specific 3D molecular generation is a promising approach for structure-based drug design. However, existing diffusion-based methods are generally restricted to marginal distributions with closed-form solutions, limiting the flexibility of generative process design, especially for the forward noising process. As a result, these methods may be less effective in modeling the complex distributions underlying pocket-conditioned molecular generation.
We develop Perturbed Flow Matching (PFM), a flow-matching-based framework for protein pocket-conditioned 3D molecular generation. PFM employs a perturbed conditional probability path that incorporates both pocket binding-site information and coupled atom type-coordinate information, enabling more effective generation of molecules in protein binding environments. On CrossDocked2020, PFM achieves competitive structural quality and the best average Vina score among the evaluated unguided methods (-7.12); PFM-G further improves the score to -8.70 with tailored classifier-free guidance. Experiments on additional molecular datasets further confirm the effectiveness and generalizability of the proposed framework.
https://github.com/kurisu92725/PFM.

PMID:
43006909
Bibliographic data and abstract were imported from PubMed on 10 Nov 2026.

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