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Immunotherapy and Immune Responses / Vaccines and Immunoinformatics Approaches / Monoclonal and Polyclonal Antibodies Research · Journal article
The Journal of Immunology · July 28, 2026
Early or partial results. Treat as a signal, not a conclusion.
ImmunoFoundation Model is a multimodal deep learning system integrating sequence, structure, and biochemical data for immunogenicity prediction, demonstrating state-of-the-art performance on cancer neoepitope benchmarks. This is early-stage computational work without clinical validation, prospective testing, or direct comparison to clinically established prediction methods.
Computational model development with retrospective benchmarking. Antigen peptide sequences, MHC allotypes (class I and II), TCR sequences, and predicted peptide-MHC complexes from public immunology databases; no human subjects enrolled. Intervention: ImmunoFoundation Model (IFM): multimodal deep learning system integrating peptide sequences, 3D molecular structures (AlphaFold3-predicted), biochemical properties, and TCR-MHC-peptide interactions.
Model trained on 300,000 samples across MHC class I and II datasets (IEDB, VDJdb, McPAS-TCR, TCR3d) Achieved state-of-the-art performance on CEDAR cancer neoepitope datasets Attention analysis distinguished between KRAS G12V and G12D mutants
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This model is not yet ready for clinical decision-making. Researchers developing immunogenicity prediction tools may consider the multimodal integration strategy, but independent external validation and prospective testing against clinical outcomes are required before clinical application.
Early-stage computational model with proof-of-concept performance on benchmark datasets but no clinical validation, prospective testing, or comparison to established clinical prediction tools in real-world settings.
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This model is not yet ready for clinical decision-making. Researchers developing immunogenicity prediction tools may consider the multimodal integration strategy, but independent external validation and prospective testing against clinical outcomes are required before clinical application.
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Abstract Introduction Predicting immunogenicity remains a critical challenge in vaccine design, autoimmunity treatment, and pharmaceutical development. Current AI tools rely on limited data inputs, typically only class-I MHC-peptide sequences, missing crucial structural and biochemical information. We developed the ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide (class-II and class-I) to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications. Methods IFM comprises three modules: (1) ESM3 transformer for embedding antigen-peptide, MHC, and TCR sequences from vast biomedical data; (2) geometric scattering transformer networks to capture molecular structure from AlphaFold3-predicted peptide-MHC complexes; (3) Autoencoder for biochemical property embedding including surface area and thermal stability. Cross-modal attention layers integrate these representations. Training utilized IEDB, VDJdb, McPAS-TCR, and TCR3d datasets totaling 300,000 samples across MHC class I and II. Results The preliminary model achieved state-of-the-art performance on CEDAR cancer neoepitope datasets. Attention mechanism analysis revealed structural motifs influencing immunogenicity, distinguishing between KRAS G12V and G12D mutants. The model successfully predicted vaccine cassette immunogenicity and identified key peptide-MHC interaction sites. Current IFM development shows improved multimodal integration with enhanced predictive accuracy across viral and cancer peptide immunogenicity tasks. Conclusion IFM represents a paradigm shift in immunogenicity prediction by comprehensively modeling the complex antigen-MHC-TCR interaction system. Its generative capabilities enable peptide optimization for cancer vaccines and personalized immunotherapy, with potential applications in autoimmunity treatment and biologics development. Funding Source Yale Colton Center for Autoimmunity Topic Categories Computational and Systems Immunology (COMP)
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