UBio-MolFM：一种通用的分子基础模型用于生物系统
UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems
AI总结：
UBio-MolFM通过引入三种创新方法，解决了量子力学精度与生物规模之间的平衡问题，实现了对大规模生物分子系统的高精度模拟。
摘要：
所有原子分子模拟作为一种本质的'计算显微镜'，用于理解生命的机制，但仍然受限于量子力学（QM）精度与生物规模之间的权衡。我们提出了UBio-MolFM，一种通用的基础模型框架，专门设计用于弥合这一差距。UBio-MolFM引入了三种协同创新：（1）UBio-Mol26，一个大型生物特定数据集，通过多保真度的'双管策略'构建，结合系统性的自底向上的列举与自顶向下的原生蛋白质环境采样（最多1,200个原子）；（2）E2Former-V2，一种线性缩放的等价变换器，整合了等价轴对齐稀疏化（EAAS）和长短期范围（LSR）建模，以捕捉非局部物理，其在大规模系统基准中的推理吞吐量可提高约4倍；（3）一个三阶段课程学习协议，从能量初始化过渡到能量-力一致性，通过以力为中心的监督来减少能量偏移。在微观力和宏观可观测量上的严格基准测试——包括液态水结构、离子溶化和肽折叠——表明，UBio-MolFM在大规模、非分布生物分子系统（最多约1,500个原子）和现实的MD可观测量上实现了从头计算级别的保真度。通过协调可扩展性与量子精度，UBio-MolFM提供了一种稳健、可直接使用的工具，用于下一代计算生物学。
英文摘要：
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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