A bio-inspired computational pipeline for antibody screening and repurposing

Therapeutic antibody discovery is central to modern drug development, yet conventional methods such as hybridoma and phage display remain slow, inefficient, and costly. Computational approaches including site-saturation mutagenesis often yield limited affinity gains and expression liabilities, while deep learning and generative models expand sequence diversity but suffer from low validation rates. Here, we present a multi-scale computational screening pipeline inspired by key principles of in vivo immune selection. The framework integrates structure-based docking (ZDock), graph neural network-based interaction prediction, and accelerated molecular dynamics (MDs) with metadynamics free-energy profiling to enable high-throughput in silico prioritization of structure-resolved antibodies. Applied to Activin A, a pleiotropic cytokine implicated in fibrosis, oncology, and muscle-wasting disorders, the platform screened ~5000 antibody structures and identified 11 candidates. Experimental validation confirmed two binders, with Ab4 exhibiting sub-nanomolar affinity (KD = 0.38 nM) and potent neutralizing activity, underscoring therapeutic potential in fibrodysplasia ossificans progressiva (FOP) and related diseases. Rather than performing full iterative affinity maturation, the present study focuses on the screening and repurposing stage, with affinity maturation positioned as a prospective extension. This work demonstrates the feasibility of integrating AI-driven interaction prediction with physics-based simulations to accelerate structure-guided antibody screening and repurposing, while conceptually paralleling selected stages of immune selection rather than fully recapitulating immune evolution.