Structure-Guided Antibody Design Through Multimodal Generative Models and Epitope-Specific Conditioning
Keywords:
antibody design; multimodal generative models; epitope-specific conditioning; protein structure prediction; computational immunology; AI governance; research infrastructureAbstract
Antibody design is shifting from expert-guided sequence optimization toward structure-aware generative systems that integrate molecular representation, immunological context, and deployability constraints. This paper examines structure-guided antibody design through multimodal generative models and epitope-specific conditioning as a socio-technical infrastructure problem rather than a purely algorithmic one. It argues that recent advances in protein structure prediction, diffusion-based design, language-model-guided sequence generation, and all-atom generative modeling create new scientific possibilities while intensifying governance, robustness, fairness, and sustainability challenges. The discussion traces how multimodal architectures combine sequence, structure, surface, and interaction signals to support antibody candidates that are not only binding-competent but also developable, manufacturable, and clinically translatable. Epitope-specific conditioning is analyzed as a control layer that constrains generative search toward functionally relevant regions, yet it also raises concerns about dataset bias, pathogen diversity, and overfitting to dominant epitope classes. The paper then considers deployment architecture, including model serving, experimental feedback loops, laboratory information systems, and reproducibility infrastructure. It further evaluates fairness in access to generative design capabilities, biosafety and dual-use risks, and the environmental footprint of large-scale training and inference. A final section addresses evaluation and clinical translation, emphasizing prospective validation, human oversight, and regulatory evidence. The conclusion recommends layered governance, open benchmarking, uncertainty-aware interfaces, and lifecycle accounting as conditions for responsible adoption. The central claim is that epitope-conditioned multimodal antibody design can become a durable public-interest platform only if model performance is coupled with institutional accountability, infrastructure equity, and sustained empirical validation.
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