MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

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ID: 315039
2026
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Abstract
MOTIVATION: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates and discrete atom types remains a challenge. Current approaches, such as diffusion and flow matching models, often fail to unify these heterogeneous modalities, relying on separate strategies or ill-fitting Euclidean metrics for discrete variables. This lack of a consistent framework limits generative models' ability to capture the geometric and chemical structure of protein-ligand complexes. RESULTS: We present MolPIF, a parameter interpolation flow mechanism designed to unify the generation of continuous and discrete molecular variables. Unlike traditional flow models that operate in sample space, MolPIF interpolates between distributions in the parameter space, theoretically recovering Wasserstein-2 optimal transport for continuous coordinates and establishing Fisher-Rao geodesics for discrete atom types. We further incorporate a geometry-enhanced learning strategy to improve the capture of atomic contexts. Extensive evaluations on the CrossDocked2020 dataset demonstrate that MolPIF outperforms baselines in binding affinity, chemical validity, geometric fidelity and chemical space coverage. Additionally, MolPIF exhibits versatility in lead optimization and offers flexible prior distribution selection (such as Laplace), establishing a robust paradigm for SBDD. AVAILABILITY: Source code is freely available at https://github.com/BLEACH366/MolPIF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W4414930042 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yaowei Jin, Junjie Wang, Wenkai Xiang, Duanhua Cao, Dan Teng, Zhehuan Fan, Jiacheng Xiong, Xia Sheng, Xia Sheng, Dong Sung An, Mingyue Zheng, Shuangjia Zheng, Qian Shi, Qian Shi
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag323
URL
https://doi.org/10.1093/bioinformatics/btag323
Keywords Keywords not found

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