Functional Characteristics Derived from the Structural Design of Bispecific Antibodies

Bispecific antibodies (bsAbs) are engineered to recognize either two distinct antigens or two different epitopes on the same antigen within a single molecule. This design varies according to the intended indication and mechanism of action; the factors considered during design are critical determinants of antigen binding, pharmacological activity, productivity, and safety. In this review, bsAbs are classified into fragment-based formats and Fc-containing IgG-like formats, with the latter further divided into symmetric and asymmetric architectures. Based on this structural framework, we discuss how key design parameters-including valency, epitope geometry, affinity and binding kinetics, and linker architecture-influence avidity, immune synapse formation, receptor clustering, signaling modulation, and toxicity profiles. We further compare preclinical and clinical examples across representative target combinations, including CD19 × CD3, CD20 × CD3, BCMA × CD3, HER2 × HER2, and EGFR × MET, to illustrate how different molecular formats yield distinct therapeutic outcomes even when the target combinations are similar. By linking structural classification with mechanism-based interpretation and within-target comparisons, this framework relates individual design variables directly to their preclinical and clinical consequences. Finally, we describe how the energy-based molecular modeling platform Rosetta and the deep-learning-based structure-prediction system AlphaFold are applied to support interface optimization, chain-pairing control, epitope geometry prediction, and structure-guided candidate prioritization. Overall, this review can provide a structure-function framework for bsAb design by integrating key structural determinants, their functional consequences, and emerging AI-based predictive strategies to facilitate the selection of optimal molecular architectures for specific therapeutic applications.