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OFAntibody is an all-atom generative framework for de novo design of monoclonal and bispecific antibodies, expanding CDR-epitope interaction learning with large-scale distilled antigen-antibody complexes and introducing multi-component structural supervision and arm-aware multi-hotspot routing. This approach enables epitope-conditioned generation across diverse bispecific formats by learning compatible multi-interface geometries and coupling paratopes to designated epitopes, significantly outperforming existing methods in candidate ranking and achieving high hotspot pass rates. OFAntibody extends de novo antibody design to programmable multi-component complexes, facilitating the creation of single molecules that recognize distinct targets and their associated biological functions.
Bispecific antibodies are now programmable with de novo design, achieving 94-100% hotspot pass rates and outperforming prior methods by over 5x in candidate ranking.
Recent advances in generative protein design have enabled de novo antibody generation with explicit target and epitope conditioning. However, most existing approaches remain formulated around a single antigen-antibody interface, whereas bispecific antibody design requires modeling multi-component complexes in which multiple target-recognition interfaces must coexist and interact within a shared antibody structure. Here we present OFAntibody, an all-atom generative framework for de novo design of monoclonal and bispecific antibodies. OFAntibody expands CDR-epitope interaction learning with large-scale distilled antigen-antibody complexes, and introduces multi-component structural supervision and arm-aware multi-hotspot routing to learn compatible multi-interface geometries and couple each antibody paratope to its designated epitope. OFAntibody supports epitope-conditioned generation across monoclonal antibodies and diverse bispecific formats, including tandem VHH, diabody and CODV. In nanobody design benchmarks, OFAntibody achieves a Top-5 enrichment rate of 41.5%, representing a 5.39-fold improvement over RFantibody in competitive candidate ranking. In bispecific antibody design tasks, OFAntibody achieves hotspot pass rates of 94-100% across the three evaluated tasks and achieves energy pass rates of 60%, 13% and 8% for diabody, tandem VHH and CODV formats, respectively. OFAntibody further enables the same target combination to be explored across different antibody formats, while joint multi-interface generation reduces geometric incompatibilities arising from independent design and post hoc assembly. Together, these results extend de novo antibody design from binary antigen-antibody complexes to programmable multi-component complexes, providing a foundation for designing single molecules that combine recognition of distinct targets and their associated biological functions.