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

TL;DR

UBio-MolFM is a universal molecular foundation model that combines a large bio-specific dataset, an efficient equivariant transformer, and a curriculum learning protocol to achieve quantum-level accuracy in large biomolecular simulations.

Contribution

The paper introduces UBio-MolFM, a novel framework integrating a new dataset, an advanced transformer architecture, and a multi-stage training protocol for scalable, accurate molecular simulations.

Findings

Achieves ab initio-level fidelity on systems up to ~1,500 atoms.

Demonstrates improved inference throughput (~4x) in large-system benchmarks.

Accurately reproduces microscopic forces and macroscopic observables.

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.