UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems

• URL: http://arxiv.org/abs/2602.17709v1
• Date: Fri, 13 Feb 2026 04:38:28 GMT
• Title: UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems
• Authors: Lin Huang, Arthur Jiang, XiaoLi Liu, Zion Wang, Jason Zhao, Chu Wang, HaoCheng Lu, ChengXiang Huang, JiaJun Cheng, YiYue Du, Jia Zhang,
• Abstract summary: We present UBio-MolFM, a universal foundation model framework designed to bridge the gap between quantum-mechanical (QM) accuracy and biological scale.<n>UBio-MolFM achieves ab initio-level fidelity on large, out-of-distribution biomolecular systems (up to 1,500 atoms) and realistic observables.
• Score: 12.633470669776317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off between quantum-mechanical (QM) accuracy and biological scale. We present UBio-MolFM, a universal foundation model framework specifically engineered to bridge this gap. UBio-MolFM introduces three synergistic innovations: (1) UBio-Mol26, a large bio-specific dataset constructed via a multi-fidelity ``Two-Pronged Strategy'' that combines systematic bottom-up enumeration with top-down sampling of native protein environments (up to 1,200 atoms); (2) E2Former-V2, a linear-scaling equivariant transformer that integrates Equivariant Axis-Aligned Sparsification (EAAS) and Long-Short Range (LSR) modeling to capture non-local physics with up to ~4x higher inference throughput in our large-system benchmarks; and (3) a Three-Stage Curriculum Learning protocol that transitions from energy initialization to energy-force consistency, with force-focused supervision to mitigate energy offsets. Rigorous benchmarking across microscopic forces and macroscopic observables -- including liquid water structure, ionic solvation, and peptide folding -- demonstrates that UBio-MolFM achieves ab initio-level fidelity on large, out-of-distribution biomolecular systems (up to ~1,500 atoms) and realistic MD observables. By reconciling scalability with quantum precision, UBio-MolFM provides a robust, ready-to-use tool for the next generation of computational biology.

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