Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.17709 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917402631995392 |
|---|---|
| author | Huang, Lin Jiang, Arthur Liu, XiaoLi Wang, Zion Zhao, Jason Wang, Chu Lu, HaoCheng Huang, ChengXiang Cheng, JiaJun Du, YiYue Zhang, Jia |
| author_facet | Huang, Lin Jiang, Arthur Liu, XiaoLi Wang, Zion Zhao, Jason Wang, Chu Lu, HaoCheng Huang, ChengXiang Cheng, JiaJun Du, YiYue Zhang, Jia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17709 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems Huang, Lin Jiang, Arthur Liu, XiaoLi Wang, Zion Zhao, Jason Wang, Chu Lu, HaoCheng Huang, ChengXiang Cheng, JiaJun Du, YiYue Zhang, Jia Chemical Physics Artificial Intelligence Biological Physics 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. |
| title | UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems |
| topic | Chemical Physics Artificial Intelligence Biological Physics |
| url | https://arxiv.org/abs/2602.17709 |