De novo design of therapeutic scFvs and multi-specific engagers from sequence alone

Abstract The discovery of functional antibody therapeutics is fundamentally constrained by the astronomical complexity of the sequence landscape and the inefficiency of empirical screening. While structure-based computational design has made significant strides, its reliance on high-resolution co-complex data restricts its utility against the vast undruggable proteome and rapidly evolving pathogens. Here, we introduce IASO, a sequence-centric generative framework for the de novo design of diverse biotherapeutic modalities, including single-chain variable fragments and multi-specific engagers, requiring only antigen sequence as input. IASO integrates an evolutionary-informed generative engine that constructs antigen-steered candidate libraries with a high-fidelity interaction module capturing synergistic physicochemical motifs. We demonstrate the framework’s robustness by identifying novel binders against diverse clinical targets, where IASO successfully discriminates atomic-level mutations to overcome established drug resistance in EGFR and distinguishes single-residue variants of SARS-CoV-2. Furthermore, we extend this approach to the in silico construction of a complex bispecific T-cell engager that exhibits developability profiles comparable to clinically approved therapeutics, including enhanced solubility and minimal immunogenicity. By transforming serendipitous exploration into predictable high-resolution engineering, IASO establishes a scalable foundation for the rapid development of next-generation biopharmaceuticals directly from sequence data alone. Competing Interest Statement The authors have declared no competing interest.