Artificial intelligence (AI)-enabled platforms for cancer vaccine engineering

Ajay K Mali; Sabya Sachi Das; Nanasaheb D Thorat

doi:10.1016/j.drudis.2026.104785

Artificial intelligence (AI)-enabled platforms for cancer vaccine engineering

Department of Physics and Bernal Institute University of Limerick

Research output: Contribution to journal › Review article › peer-review

Abstract

Cancer vaccines are emerging as a promising approach in immuno-oncology, with the potential to generate tumour-specific and long-lasting immune responses. However, shared-antigen strategies often show limited efficacy because of tumour heterogeneity, immune escape and challenges in identifying immunogenic targets, ultimately limiting progress towards effective immunotherapies. Recent breakthroughs in artificial intelligence (AI) are facilitating a shift towards systematic and data-driven vaccine design, effectively bridging conventional design gaps. AI-driven integration of immunopeptidomics, sequence optimisation and delivery design supports a more cohesive therapeutic pipeline. This review highlights recent advances, key challenges and the scientific and translational considerations shaping next-generation cancer vaccines.

Original language	English
Pages (from-to)	104785
Journal	Drug Discovery Today
DOIs	https://doi.org/10.1016/j.drudis.2026.104785
Publication status	E-pub ahead of print - 27 Aug 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1016/j.drudis.2026.104785

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title = "Artificial intelligence (AI)-enabled platforms for cancer vaccine engineering",

abstract = "Cancer vaccines are emerging as a promising approach in immuno-oncology, with the potential to generate tumour-specific and long-lasting immune responses. However, shared-antigen strategies often show limited efficacy because of tumour heterogeneity, immune escape and challenges in identifying immunogenic targets, ultimately limiting progress towards effective immunotherapies. Recent breakthroughs in artificial intelligence (AI) are facilitating a shift towards systematic and data-driven vaccine design, effectively bridging conventional design gaps. AI-driven integration of immunopeptidomics, sequence optimisation and delivery design supports a more cohesive therapeutic pipeline. This review highlights recent advances, key challenges and the scientific and translational considerations shaping next-generation cancer vaccines.",

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AU - Das, Sabya Sachi

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N2 - Cancer vaccines are emerging as a promising approach in immuno-oncology, with the potential to generate tumour-specific and long-lasting immune responses. However, shared-antigen strategies often show limited efficacy because of tumour heterogeneity, immune escape and challenges in identifying immunogenic targets, ultimately limiting progress towards effective immunotherapies. Recent breakthroughs in artificial intelligence (AI) are facilitating a shift towards systematic and data-driven vaccine design, effectively bridging conventional design gaps. AI-driven integration of immunopeptidomics, sequence optimisation and delivery design supports a more cohesive therapeutic pipeline. This review highlights recent advances, key challenges and the scientific and translational considerations shaping next-generation cancer vaccines.

AB - Cancer vaccines are emerging as a promising approach in immuno-oncology, with the potential to generate tumour-specific and long-lasting immune responses. However, shared-antigen strategies often show limited efficacy because of tumour heterogeneity, immune escape and challenges in identifying immunogenic targets, ultimately limiting progress towards effective immunotherapies. Recent breakthroughs in artificial intelligence (AI) are facilitating a shift towards systematic and data-driven vaccine design, effectively bridging conventional design gaps. AI-driven integration of immunopeptidomics, sequence optimisation and delivery design supports a more cohesive therapeutic pipeline. This review highlights recent advances, key challenges and the scientific and translational considerations shaping next-generation cancer vaccines.

U2 - 10.1016/j.drudis.2026.104785

DO - 10.1016/j.drudis.2026.104785

M3 - Review article

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SN - 1359-6446

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JO - Drug Discovery Today

JF - Drug Discovery Today

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Artificial intelligence (AI)-enabled platforms for cancer vaccine engineering

Abstract

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