PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation

• URL: http://arxiv.org/abs/2503.21788v3
• Date: Tue, 01 Apr 2025 02:12:44 GMT
• Title: PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation
• Authors: Yizhen Luo, Jiashuo Wang, Siqi Fan, Zaiqing Nie,
• Abstract summary: We propose PharMolixFM, a unified framework for constructing all-atom foundation models.<n>Our framework includes three variants using state-of-the-art multi-modal generative models.<n>PharMolixFM-Diff achieves competitive prediction accuracy in protein-small-molecule docking.
• Score: 4.402280157389038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While recent advances in deep learning have enabled the development of all-atom foundation models for molecular modeling and generation, existing approaches face challenges in generalization due to the multi-modal nature of atomic data and the lack of comprehensive analysis of training and sampling strategies. To address these limitations, we propose PharMolixFM, a unified framework for constructing all-atom foundation models based on multi-modal generative techniques. Our framework includes three variants using state-of-the-art multi-modal generative models. By formulating molecular tasks as a generalized denoising process with task-specific priors, PharMolixFM achieves robust performance across various structural biology applications. Experimental results demonstrate that PharMolixFM-Diff achieves competitive prediction accuracy in protein-small-molecule docking (83.9% vs. 90.2% RMSD < 2{\AA}, given pocket) with significantly improved inference speed. Moreover, we explore the empirical inference scaling law by introducing more sampling repeats or steps. Our code and model are available at https://github.com/PharMolix/OpenBioMed.

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