Physics-informed deep learning: A new paradigm for macromolecular recognition and rational protein design
TL;DR
By delineating how physics-based priors synergize with data-driven representation learning, this review provides a comprehensive roadmap for generating physically plausible and thermodynamically stable therapeutics, ultimately accelerating the transition of computationally designed molecules from in silico blueprints to viable clinical candidates.
Abstract
Targeted drug discovery is fundamentally bottlenecked by the challenge of accurately modeling complex biomolecular interactions, ranging from small-molecule ligand binding to high-order macromolecular assemblies. While traditional physics-based computational methods provide profound mechanistic insights, their clinical utility is frequently hampered by prohibitive computational costs and scalability limitations when addressing highly flexible, cross-scale systems. Conversely, the rapid emergence of pure deep learning offers unprecedented computational speed but suffers from a fundamental “black-box” nature, sometimes yielding physically improbable conformations—often referred to as “hallucinations”—that can pose challenges in real-world experimental validation. To bridge this critical translational gap, the integration of physical principles with artificial intelligence—Physics-Informed Deep Learning (PIDL)—is currently driving a fundamental transition from purely empirical approximations to rational, physically grounded design. This review constructs a strategic framework to critically evaluate these transformative advances, structured around three methodological pillars: (1) Physics-constrained optimization, which integrates thermodynamic principles and integrative experimental restraints at the output level to decode macromolecular dynamics; (2) Physics-encoded architectures, which embed appropriate SE(3) or E(3) geometric symmetries directly into neural network topologies for precise structural recognition; and (3) Physics-guided representations, which project discrete sequences into continuous physicochemical manifolds to enhance interaction prediction. By delineating how physics-based priors synergize with data-driven representation learning, this review not only synthesizes current algorithmic breakthroughs but also provides a comprehensive roadmap for generating physically plausible and thermodynamically stable therapeutics, ultimately accelerating the transition of computationally designed molecules from in silico blueprints to viable clinical candidates.