Physics-Informed Deep Learning: A New Paradigm for Macromolecular Recognition and Rational Protein Design

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, often yielding physically inaccessible ``hallucinations'' that fail in real-world 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 enforces thermodynamic laws and integrative experimental restraints at the output level to decode macromolecular dynamics; (2) Physics-encoded architectures, which embed strict $SE(3)/E(3)$ 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 thermodynamically stable therapeutics, ultimately accelerating the transition of computationally designed molecules from in silico blueprints to viable clinical candidates.