Can Machines Design Antibodies Faster and Better than Biology?

We present a unified computational approach that leverages AI-based Protein Language Models (PLMs) and a structure prediction AI co-folding model with Free Energy Perturbation (FEP) calculations to enable fast and accurate antibody design. We demonstrate the platform's predictive power through retrospective validation across diverse antibody-antigen systems, achieving 97% enrichment in identifying high-affinity binders from sequence libraries comprising hundreds of candidates. More compelling is prospective validation: PLM-derived surrogate models were deployed to design a tetraspecific secondary antibody—a multi-parameter optimization problem that traditional workflows would address through years of iterative screening. Our platform delivered three novel lead candidates in a single design round, with an 80%experimental expression rate.

The path forward is not better AI or better physics. It is their deliberate, bidirectional integration—where physical laws constrain generative models and learned representations accelerate free energy calculations.