Physics-Informed Artificial Intelligence Design of Picomolar Nanobodies Enables Deep Tumor Penetration and High-Contrast Imaging.

The clinical utility of nanobodies in solid tumor therapy is constrained by a fundamental biophysical trade-off: rapid renal clearance necessitates half-life extension, which in turn demands ultrahigh affinity to prevent dissociation from the target under systemic washout conditions. While generative artificial intelligence has substantially advanced structure prediction, it often fails to resolve the subtle energetic frustrations at protein-protein interfaces required for affinity maturation. Here, we present a physics-informed artificial intelligence framework that integrates AlphaFold 3 structural priors with molecular dynamics simulations to rationally design a picomolar anti-carcinoembryonic antigen nanobody. By employing variable dielectric molecular mechanics/generalized Born surface area decomposition, we identified interfacial residues that were structurally permissible but thermodynamically suboptimal. We subsequently constructed a focused library to resolve these bottlenecks through electrostatic optimization, desolvation penalty minimization, and van der Waals packing refinement. This strategy achieved a 99% binding positivity rate and yielded variants with picomolar affinity (