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Review
. 2025 Jun 4;36(3):102586.
doi: 10.1016/j.omtn.2025.102586. eCollection 2025 Sep 9.

Deep learning in next-generation vaccine development for infectious diseases

Manojit Bhattacharya  1 , Yi-Hao Lo  2   3 , Srijan Chatterjee  4 , Arpita Das  5 , Zhi-Hong Wen  6   7 , Chiranjib Chakraborty  5
Affiliations
Review

Deep learning in next-generation vaccine development for infectious diseases

Manojit Bhattacharya et al. Mol Ther Nucleic Acids. .

Abstract

The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast.

Keywords: MT: Bioinformatics; deep learning; immunoinformatics; infectious diseases; next-generation vaccine.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Timeline showed the paradigm shift in vaccine development using DL applications and advanced vaccine development
Figure 2
Figure 2
Application of DL for epitope prediction, through the improved accuracy, handling large datasets, and pan-specificity
Figure 3
Figure 3
DL feature extraction properties have been used for potential immunogenic epitopes extraction or epitopes mapping from the amino acids sequences
Figure 4
Figure 4
The DL model classification and categorization into three broad categories: supervised, semi-supervised, and unsupervised
Figure 5
Figure 5
The examples of collective DL model architectures, which include inherent complexity, are well suited for complex patterns and can involve large datasets
See this image and copyright information in PMC

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