Synthetic Engineering with ImmunoGenAI: Multimodal Deep Learning Integration for Predicting and Optimizing Immune Evasion in Personalized Therapeutic Devices

ImmunoGenAI characterizes immunogenicity as an emergent property influenced by the intracellular antigen-processing pathway, rather than an isolated function of peptide–HLA binding affinity. It is informed by biological processes including proteasomal cleavage, peptide transport, and HLA presentation, which shape downstream T-cell recognition. ImmunoGenAI optimizes existing therapeutic and vaccine sequences by introducing targeted sequence perturbations that reduce predicted unintended immunogenic peptide formation. These include modifying residues associated with cleavage patterns and HLA binding preferences to lower peptide–HLA stability while preserving structural integrity. By acting across multiple biologically relevant features simultaneously, these changes reduce immunogenicity more effectively than single-objective optimization. This framework is bidirectional: it suppresses immune visibility for therapeutic design and can be inverted for vaccine design to remove unnecessary amino acid substitutions, enabling more reliable control over sequence quality and downstream performance. ImmunoGenAI enables systematic control of immune recognition, outperforming five other industry-standard tools while maintaining structural integrity. ImmunoGenAI utilizes modality-specific neural network encoders, including contrastive and transformer architectures, to project biological inputs into unified latent representations.