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.