Beyond Affinity: AI-Supported Developability Assessment and Multi-Objective Optimization in Antibody Development

Abstract Therapeutic antibodies are a major class of medicines, but high target affinity alone does not ensure manufacturability, stability, safety, or clinical success. Developability has therefore become a central constraint in antibody engineering and has pushed the field beyond affinity-first screening toward multi-objective decision-making. Machine-learning-based models increasingly integrate antibody sequence, structure, interaction, and assay data to estimate properties such as affinity, specificity, aggregation, viscosity, solubility, stability, immunogenicity, and pharmacokinetics before experimental testing. In practice, these models are most useful when they help prioritize experiments rather than replace empirical evaluation. Here, we review the data resources used for antibody developability modeling, the main classes of property predictors, and optimization frameworks that balance competing design objectives. We cover Pareto optimization, Bayesian optimization, active learning, and conditional generative modeling, and discuss how these approaches are being adapted to bispecific antibodies and nanobodies. We argue that AI is most useful when model outputs are interpreted in the context of assay design, uncertainty, and antibody format, and when they are used to guide candidate selection and experimental design rather than serve as stand-alone surrogates for developability.