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

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 < 2Å, 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 atthis https URL.

@article{luo2025_2503.21788,
  title={ PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation },
  author={ Yizhen Luo and Jiashuo Wang and Siqi Fan and Zaiqing Nie },
  journal={arXiv preprint arXiv:2503.21788},
  year={ 2025 }
}