AI-driven epitope prediction: a systematic review, comparative analysis, and practical guide for vaccine development

Abstract

Integrating AI into epitope prediction is transforming vaccine design by delivering unprecedented accuracy, speed, and efficiency. This review synthesizes recent breakthroughs particularly CNNs, transformers, and GNNs highlighting experimentally validated models like MUNIS and GraphBepi that reveal previously overlooked epitopes. By benchmarking AI tools against traditional methods, we identify structural data integration as pivotal, offering practical strategies to translate computational predictions into actionable experimental workflows for next-generation vaccines.

Introduction

Given current global challenges such as emerging pandemics and antimicrobial resistance, vaccines are more crucial today than ever^1,2. Traditional vaccine development is a protracted and high-risk endeavor. On average, bringing a novel vaccine from concept to market requires 10 years of research and development (R&D) and nearly $900 million in investment^3,4. The attrition rate is daunting: over 90% of vaccine candidates fail somewhere between preclinical studies and licensure⁵. This lengthy timeline and high failure rate inflate costs and delay responses to emerging pathogens. The unprecedented success of COVID-19 vaccines, however, demonstrates how massive funding and accelerated trials can substantially shorten these timelines, underscoring the exceptional nature of such rapid progress^6,7.

AI technologies have gradually emerged as game-changers in biomedical research, building upon decades of computational biology and immunoinformatics. Early groundwork in the 2000s introduced reverse vaccinology, wherein entire pathogen genomes were screened in silico to identify vaccine antigens⁸. In a landmark 2000 study, Pizza et al. mined the genome of Neisseria meningitidis B, predicted hundreds of candidate surface antigens, and experimentally tested 350 of them in mice, a scale of antigen discovery impossible by conventional methods^9–11. This demonstrated how computation could massively expand candidate diversity and uncover novel targets. In subsequent years, machine learning (ML) began to assist in epitope mapping and antigen design. For instance, neural-network algorithms were applied to predict T-cell epitopes (e.g., the NetMHC series), marking one of the first forays of AI into vaccinology^12–14. However, early models had limited accuracy, and studies revealed that standard epitope prediction tools could be inconsistent. In one SARS-CoV-2 study, only 174 out of 777 computationally predicted HLA-binding peptides were confirmed to bind stably in vitro^15,16. These limitations set the stage for the development of more advanced AI methods. Over the last decade, the rise of deep learning and big data has significantly enhanced predictive power in biomedicine. Notably, breakthroughs like DeepMind’s AlphaFold have effectively “solved” the protein folding problem, providing high-quality structural models for millions of proteins and empowering structure-based vaccine design. In parallel, modern deep learning models have begun to tackle the complexity of immune recognition and biologic drug design in ways previously unattainable¹⁷.

Recent studies demonstrate that AI-driven approaches have significantly increased predictive accuracy. For example, a deep learning model for B-cell epitope prediction achieved 87.8% accuracy (AUC = 0.945) and outperformed previous state-of-the-art methods by about 59% in Matthews correlation coefficient¹⁸. Likewise, a new T-cell epitope predictor (MUNIS) showed a 26% higher performance than the best prior algorithm. Notably, these models translate predictions into real immunological insights: the MUNIS framework successfully identified known and novel CD8⁺ T-cell epitopes from a viral proteome, experimentally validating them through HLA binding and T-cell assays¹⁹. In other words, AI algorithms not only achieve high benchmark performance but also successfully identify genuine epitopes that were previously overlooked by traditional methods, thus providing a crucial advancement toward more effective antigen selection²⁰.

Adopting AI in vaccine development offers compelling advantages at multiple stages of the pipeline. First, AI can dramatically accelerate discovery. Rather than physically screening thousands of candidates, models can learn from large datasets to prioritize the most promising antigens or drug leads in silico²¹. During the COVID-19 crisis, for example, traditional bioinformatics methods such as sequence alignment with BLAST rapidly analyzed the SARS-CoV-2 genome to identify the Spike protein as a key vaccine target, with subsequent stages of vaccine design and optimization benefiting significantly from machine learning algorithms^6,22.

ML-based reverse-vaccinology platforms (e.g., Vaxign-ML) even suggested less obvious targets. Ong et al. reported that an AI pipeline flagged the coronavirus nsp3 protein (a large nonstructural protein not included in earlier vaccines) as a high-value antigen candidate due to its conserved, immunogenic regions. Such data-driven approaches extend the antigen search beyond traditionally focused areas, potentially increasing the diversity of vaccine candidates. Indeed, AI-enabled scans of pathogen proteomes have identified dozens of candidate antigens at once, including novel targets that conventional methods would likely overlook²². Second, AI systems can enhance prediction accuracy for critical properties, such as antigenicity, immunogenicity, and safety²³. Recent deep learning models leverage burgeoning immunological datasets; for example, a 2025 study assembled >650,000 human HLA–peptide interactions, achieving substantially higher accuracy in T-cell epitope prediction than prior tools. The authors’ model (MUNIS) outperformed existing algorithms in identifying HLA class I-presented viral peptides and correctly predicted immunodominant epitopes in Epstein–Barr virus, including novel epitopes experimentally validated via in vitro T-cell assays¹⁹. Remarkably, the AI’s immunogenicity predictions were on par with results from laboratory binding assays, suggesting that deep learning can substitute for specific wet-lab screens and thereby reduce experimental burden and attrition in early vaccine discovery¹⁹.

A prominent example is advanced AI tools, such as graph neural networks (GNNs), which have successfully optimized vaccine antigens targeting SARS-CoV-2 variants²⁴. Specifically, the GearBind GNN facilitated computational optimization of spike protein antigens, resulting in antigen variants with substantially enhanced binding affinity, up to 17-fold higher, for neutralizing antibodies, as confirmed by ELISA assays, after synthesizing and experimentally validating only 20 candidates. Crucially, these AI-optimized antigens maintained or improved broad-spectrum neutralization against multiple viral variants, demonstrating AI’s ability to enhance vaccine potency and significantly broaden protective coverage while reducing experimental efforts^6,25.

For experimental immunologists, these advances in AI offer a powerful opportunity, but also present a new landscape of tools and techniques to navigate. This review is designed as a practical guide for incorporating AI into vaccine R&D workflows. In contrast to prior reviews that primarily extol AI’s potential, we focus on providing actionable insights for bench scientists. We provide curated comparisons of AI platforms for epitope prediction and antigen design, highlighting their data requirements and performance metrics in real-world scenarios. We summarize case studies where AI-driven vaccine design has led to successful candidates or notable improvements, emphasizing experimental validation of predictions. Finally, we outline strategies for experimental validation of AI-generated vaccine targets, from in vitro immunogenicity assays to in vivo challenge models, to help researchers rigorously confirm algorithmic predictions. By bridging computational advances with immunological know-how, this review aims to equip vaccine researchers with a roadmap for leveraging AI to speed up development, reduce failure rates, and ultimately design better vaccines.

Epitope identification and its importance in vaccines

An epitope is a specific region of an antigen recognized by the immune system. B-cell epitopes are protein regions bound by antibodies, while T-cell epitopes are short peptides presented on MHC molecules recognized by T-cell receptors²⁶. Accurate identification of these epitopes is crucial for vaccine development, enabling researchers to design vaccines that elicit targeted immune responses. For example, cytotoxic CD8 + T cells require peptide epitopes (8–11 amino acids) presented on infected cells, essential for immunity against viruses like HIV and SARS-CoV-2. Similarly, B-cell epitopes (often conformational surface regions) induce neutralizing antibodies, critical for vaccine efficacy. Computational (in silico) prediction of epitopes significantly accelerates vaccine research and development, thereby reducing the requirements for experimental screening. Recent deep learning models, such as MUNIS, demonstrate that computational predictions can match experimental accuracy, streamlining vaccine development by rapidly identifying protective epitopes¹⁹.

Traditional epitope identification relied on experimental screening and basic computational heuristics, each with notable limitations. Motif-based methods for identifying T-cell epitopes have been sought after for known peptide patterns, but they often fail to detect novel alleles or unconventional epitopes. Homology-based methods relied on sequence similarity, often missing novel or divergent proteins. For B-cell epitopes, early computational approaches using physicochemical scales or sequence conservation achieved low accuracy (~50–60%), as many epitopes are conformational and not linear. Experimental methods, such as peptide microarrays or mass spectrometry, are accurate but slow and costly^27,28. Additionally, traditional methods faced data scarcity and lacked definitive negative controls. Consequently, conventional methods often yield ambiguous or unreliable predictions, as they are unable to capture the complexity required to accurately predict immunogenic epitopes^19,29,30.

Modern AI, especially deep learning, has revolutionized epitope prediction by learning complex sequences and structural patterns from large immunological datasets. Unlike motif-based rules, deep neural networks can automatically discover nonlinear correlations between amino acid features and immunogenicity. Both B-cell and T-cell epitope predictions have benefited from these advances:

Convolutional Neural Networks (CNNs)

Convolutional neural networks (CNNs) have been successfully applied to predict T-cell epitopes for vaccine design³¹. One approach, exemplified by Deepitope, ignores explicit HLA restrictions, treating all experimentally validated immunogenic peptides as a single positive class, achieving an ROC AUC of ~0.59, comparable to HLA-specific predictors (NetMHCIIpan). Incorporating a BiLSTM further enhanced ROC AUC to ~0.70^30,32. Conversely, models like DeepImmuno-CNN explicitly integrate HLA context, processing peptide–MHC pairs with convolutional layers and rich physicochemical features, markedly improving precision and recall across diverse benchmarks, including SARS-CoV-2 and cancer neoantigen datasets^30,33. Importantly, these CNN models yield biologically interpretable outputs, highlighting critical residues that drive T-cell recognition. CNNs have also significantly advanced B-cell epitope prediction. NetBCE, combining CNN and bidirectional LSTM with attention mechanisms, achieved a cross-validation ROC AUC of ~0.85, substantially outperforming traditional tools³⁴. Likewise, DeepLBCEPred, which utilizes BiLSTM and multi-scale CNNs with attention, demonstrated significant improvements in accuracy and MCC compared to classic predictors such as BepiPred and LBtope^34,35. These results emphasize the capability of CNN-based models to extract meaningful patterns from epitope data, facilitating robust and interpretable vaccine design. The CNN architecture is detailed in Fig. 1, illustrating the processing flow from input quantization through convolutional and pooling layers to fully connected layers.

Fig. 1. Schematic representation of a convolutional neural network (CNN) architecture for processing sequential input data.

The input is first quantized and transformed into a frame-based representation. This is followed by multiple layers of convolution and max-pooling operations that extract hierarchical features. The resulting feature maps are progressively condensed and passed through fully connected layers to produce the final output. The figure illustrates the spatial reduction and feature abstraction process inherent to CNNs applied to time-series or text-based representations. In vaccine design, CNNs are particularly useful for detecting conserved local sequence motifs characteristic of linear B-cell epitopes and short T-cell epitopes, facilitating rapid and accurate identification of antigenic regions suitable for inclusion in peptide-based vaccine candidates.

Recurrent Neural Networks (RNNs) and LSTMs

RNN-based models have been utilized to predict peptide epitopes that bind to Major Histocompatibility Complex (MHC) molecules and elicit T-cell responses³⁶. For instance, MHCnuggets employs an LSTM network to predict peptide–MHC affinity for class I and II alleles, achieving a fourfold increase in predictive accuracy over earlier methods validated by mass spectrometry. MHCnuggets also demonstrated computational efficiency, rapidly evaluating ~26.3 million peptide–allele pairs in about 2.3 h³⁷. Additionally, LSTM-based predictors, such as the BigMHC ensemble, have achieved high AUC values and improved precision for neoantigen prediction compared to previous tools²⁶.

RNNs have also improved the prediction of T-cell receptor (TCR)–epitope specificity. A 2022 study introduced a hybrid Attention-BiLSTM-CNN model for classifying TCRs that bind to specific epitopes. On the McPAS-TCR dataset, this model achieved an AUC of 0.974 for recognizing whether any TCR can bind a given epitope (“naïve” binding prediction) and an AUC of 0.887 for predicting specific TCR–epitope pairs. These AUCs exceeded those of previous models, such as TCRGP, ERGO, and NetTCR, indicating that integrating bidirectional LSTMs (which capture sequence context from both N- and C-termini) with attention mechanisms (focusing on critical motif positions) and CNN layers (extracting local sequence motifs) yields state-of-the-art TCR–epitope prediction. Such models help identify which epitopes are likely immunogenic by TCR recognition³⁸.

RNN architectures have also improved linear B-cell epitope prediction. Earlier tools, such as ABCpred, showed moderate accuracy (~65–67%)³⁹. More advanced models integrate RNNs with additional layers to boost performance. For example, DeepLBCEPred combines BiLSTM, feed-forward attention, and multi-scale CNNs, achieving ~0.67 accuracy and ~0.35 MCC on IEDB benchmarks, slightly surpassing previous methods^35,40. Ablation studies highlighted the BiLSTM’s crucial role in specificity. Similarly, an attention-augmented LSTM model by Noumi et al. outperformed the widely used BepiPred 2.0 by effectively capturing distant sequence features⁴¹.

Modern epitope predictors often merge RNNs with other architectures. In addition to the CNN and attention hybrids above, researchers are fusing RNNs with graph neural networks to leverage 3D structural insights. For example, GraphBepi (2023) couples a BiLSTM-based sequence encoder with a GNN that operates on antigen 3D structures^42,43. Using AlphaFold2-predicted structures, GraphBepi improved epitope prediction AUC by >5.5% (up to 44% on some metrics) over state-of-the-art sequence-only methods. In GraphBepi’s design, the BiLSTM (or a pre-trained language model) processes the amino acid sequence to provide contextual embeddings, which are then input to a graph network that models spatial residue–residue interactions⁴². This combination of sequence context and structural context exemplifies how RNNs are integrated into hybrid models to boost accuracy in epitope discovery. Overall, the use of LSTMs in epitope prediction, whether for MHC binding, TCR specificity, or B-cell epitope identification, has yielded competitive or state-of-the-art performance (often with an AUC in the 0.8–0.99 range, high sensitivities, etc.), especially when combined with attention or other network modules^38,44,45. Fig. 2 presents a comparative schematic of a standard RNN unit and an LSTM unit, highlighting the key architectural differences and gating mechanisms that enable improved handling of long-term dependencies in sequential data.

Fig. 2. Comparison between a standard Recurrent Neural Network (RNN) unit and a Long Short-Term Memory (LSTM) unit.

While the RNN unit processes input based solely on the current input and the previous hidden state, the LSTM architecture introduces gating mechanisms (input, forget, and output gates) that regulate the flow of information. These gates allow the LSTM to retain long-term dependencies by controlling what information to add, maintain, or discard from the cell state. This enables more stable learning across long sequences, mitigating the vanishing gradient problem commonly observed in traditional RNNs. In vaccine design, such capability is especially valuable, as LSTM models can capture extended sequence dependencies critical for accurately predicting T-cell epitopes, which rely on the precise arrangement of residues for binding to MHC molecules. x_t: input at time t, h_t: hidden state at time t, c_t: cell state at time t, f: forget gate activation, i_t: input gate activation, o_t: output gate activation, g_t (or g): candidate cell input (input modulation gate), σ: sigmoid activation function, φ (or θ): tanh activation function.

CNNs, RNNs, and attention-based models each offer distinct advantages for epitope prediction. CNNs excel at identifying local sequence motifs, making them particularly effective for detecting short, conserved patterns in linear B-cell epitopes and MHC-binding peptides^35,46. In contrast, RNNs, especially LSTMs, are better suited for modeling longer sequences, as they capture extended contextual dependencies important for T-cell epitope recognition⁴⁷. Attention-based models, such as transformers, outperform both CNNs and RNNs by globally assessing antigen sequences and identifying critical residues, irrespective of their sequence positions. This capability enables transformers to detect subtle, long-range interactions that CNNs and RNNs may overlook, resulting in higher accuracy for both T-cell and B-cell epitope prediction, and thus facilitating more reliable vaccine target selection⁴⁸.

Transformer and attention-based models

Transformer-based architectures have emerged as powerful tools in vaccine design, applied to both T-cell (MHC-presented) and B-cell epitope prediction. Models such as BERTMHC, ProteinBERT, TAPE, VenusVaccine, and the ESM family utilize self-attention mechanisms to learn complex patterns in immunological sequences^49,50. These networks treat amino acid sequences like natural language, using multi-head attention to capture long-range dependencies and key motifs critical for immune recognition. For example, BERTMHC (a BERT-derived model fine-tuned for pan-specific MHC binding) identifies critical residues for peptide–MHC class II binding, even highlighting anchor positions in the MHC binding pockets via its attention weights^50,51. Transformer models provide a context-aware representation of epitopes that was unattainable with earlier sequence-based methods^52–54.

Attention-based models consistently outperform prior architectures (e.g., CNNs, RNNs, or random forests) on epitope prediction benchmarks in comparative evaluations. For instance, BERTMHC improved MHC class II binding prediction (achieving AUC ~ 0.882 vs ~0.877 by the previous best NetMHCIIpan) and its class I counterpart (ImmunoBERT) similarly leveraged transformer embeddings to enhance CTL epitope prediction, with attention analysis confirming the importance of peptide N- and C-terminal residues for binding⁵⁵. More advanced transformer architectures have yielded even larger gains: a cross-attention model (CapTransformer) surpassed NetMHCpan4.0 in predictive accuracy by jointly attending to peptide–MHC features, while models fine-tuned from large protein language models (e.g., MHCRoBERTa using ProtBERT, or ESM-GAT using ESM embeddings plus graph attention) have outperformed state-of-the-art tools like NetMHCpan4.1⁵⁰. Notably, similar breakthroughs are seen in B-cell epitope prediction. A recent transformer-based model achieved ~81% accuracy (AUC ≈ 0.90) for parasite B-cell epitope identification, significantly higher than the ~0.78 AUC of the best conventional classifier on the same test data⁴⁸. Such improvements across diverse metrics (ROC–AUC, accuracy, etc.) underscore the superior sensitivity and generalization of transformers over earlier methods.

Beyond improved metrics, these attention-based models offer practical benefits for vaccine development. Many provide biologically interpretable insights; for example, VenusVaccine’s attention weights highlight sequence regions that overlap with known antigenic sites (e.g., correctly pinpointing the SARS-CoV-2 spike protein’s receptor-binding domain as an immunodominant region)⁴⁹. Some predicted epitopes from these models have been experimentally corroborated, underscoring their utility in guiding the selection of real-world vaccine candidates. By capturing the subtle sequence patterns that determine immunogenicity, transformer-driven predictors now represent the state-of-the-art MHC class I/II and B-cell epitope prediction, combining high predictive performance with mechanistic insight into antigenic determinants. Figure 3 illustrates the core architecture of the Transformer model, highlighting the encoder–decoder structure and key components such as multi-head attention, positional encoding, and feed-forward layers.

Fig. 3. Schematic overview of the transformer architecture for sequence-to-sequence learning.

The model consists of an encoder (left) and a decoder (right), each composed of stacked layers that integrate multi-head self-attention mechanisms, feed-forward networks (MLPs), and residual connections, followed by layer normalization (Add & Norm). Positional encodings are added to the input embeddings to preserve order information. The decoder includes an additional masked multi-head attention module to ensure autoregressive behavior during training. Final outputs are projected through a linear layer followed by a softmax function to generate token-level probabilities. In vaccine design, Transformer architectures provide a significant advantage by identifying critical immunogenic motifs and long-range interactions across antigen sequences, thereby greatly improving the accuracy and reliability of predicted T-cell and B-cell epitopes.

Graph Neural Networks (GNNs) for structure

Recent work has demonstrated that graph neural networks (GNNs) improve structure-based B-cell epitope prediction compared to classical methods, such as DiscoTope and SEPPA. Traditional 3D epitope predictors rely on geometric heuristics or propensity scales, with moderate accuracy (e.g., DiscoTope 2.0 ROC-AUC ≈ 0.66; SEPPA 3.0 ≈ 0.75)^56,57. In contrast, GNN-based models leverage protein structure graphs to capture complex spatial patterns of epitope residues. For example, GraphBepi (2023) integrates AlphaFold2-predicted structures with an edge-enhanced GNN and BiLSTM sequence encoder, yielding substantial gains that outperform prior methods by more than 5.5% in ROC-AUC and 44% in precision–recall AUC⁴³. Likewise, EpiGraph (2024) employs a graph attention network (GAT) on combined structure and sequence embeddings (from ESM-IF1 and ESM-2) to learn spatially clustered epitope features. EpiGraph and GraphBepi consistently outperformed state-of-the-art tools (BepiPred-3.0, DiscoTope-3.0), achieving higher precision-recall scores on benchmark datasets (AUC_PR ≈ 0.23–0.24 vs ~0.19 for DiscoTope)^27,43. Notably, these GNN models offer mechanistic interpretability: EpiGraph’s attention layers exploit the inherent clustering of epitope residues on the antigen surface (high graph homophily), as evidenced by significantly higher connectivity among predicted epitope residues^27,58. In practical terms, the GNN learns to focus on surface patches where antigenic residues congregate, aligning with experimental observations and providing intuitive visual maps of putative epitope regions.

GNN methods have improved T-cell epitope prediction by structurally modeling peptide–MHC and TCR interactions. GraphMHC (2024) simulates MHC–peptide complexes as 3D atomic interaction graphs processed by graph attention and convolution layers, achieving high accuracy (ROC-AUC ~ 0.92) and surpassing sequence-based approaches^59–61. Its attention mechanism identifies critical residues in the peptide–MHC interface, enhancing interpretability. Similarly, HeteroTCR (2024) employs heterogeneous graphs of TCR and peptide sequences, significantly outperforming previous models (improved ROC-AUC on multiple datasets) through multi-hop graph message passing and attention mechanisms that highlight essential TCR–peptide interaction features⁶².

These GNN-driven advances represent a new paradigm in vaccine design, combining structural data with deep learning to enhance prediction accuracy and interpretability (Choi and Kim, 2024). Attention mechanisms identify surface residues or peptide motifs most likely to be immunogenic, guiding antigen selection and engineering with clear rationales^63,64. Such interpretable GNN models connect computational predictions to immunological mechanisms, improving next-generation vaccine development. Figure 4 illustrates the GNN architecture, highlighting the passing message and nonlinear transformations applied to node representations.

Fig. 4. Schematic representation of a Graph Neural Network (GNN) architecture with two hidden layers.

The model operates on graph-structured data, where each node aggregates information from its neighbors through message-passing operations. At each layer, node representations are updated and transformed via nonlinear activation functions (in this case, ReLU), enabling the network to learn expressive features from the graph topology and node attributes. The final output layer produces the refined node representations or graph-level predictions. Hidden layers represent intermediate node embeddings; arrows denote message passing and aggregation across graph edges; ReLU Rectified Linear Unit activation function. In the context of vaccine design, GNN architectures are particularly useful for predicting conformational B-cell epitopes, as they effectively leverage three-dimensional structural information of antigens to identify spatially clustered immunogenic regions critical for antibody binding.

Case study: venusvaccine: a dual-attention deep learning system

One illustrative example of modern AI in vaccine design is VenusVaccine, a deep learning system specifically developed for epitope-based immunogenicity prediction. The architecture and workflow of VenusVaccine are summarized in Fig. 5. VenusVaccine’s architecture employs a “dual attention mechanism” to integrate two data modalities: protein sequence and structure. In practice, the model encodes the amino acid sequence of an antigen as well as its 3D structural representation (the latter obtained from experimental data or predictions). The dual self-attention layers allow the model to weigh informative features in the sequence (e.g., motifs, glycosylation sites) alongside features in the structure (e.g., surface accessibility, spatial motifs), effectively “learning” how sequence context and folding geometry jointly contribute to immune recognition⁴⁹. After the attention layers, VenusVaccine aggregates information with additional fully connected layers and physicochemical feature inputs to finally output a binary classification: whether the antigen (or a region of it) is likely immunogenic (contains protective epitopes) or not.

Fig. 5. Schematic illustration of the VenusVaccine deep learning framework for epitope prediction and immunogenicity assessment.

The model integrates peptide tokenization, atomic structure tokenization, amino acid (AA) sequence embedding, and physicochemical descriptors. It employs a hierarchical attention mechanism consisting of finer-level and coarser-level cross-attention modules, each incorporating rotary position embedding (RoPE) and dot-product attention, effectively capturing detailed sequence and structural features. An attention pooling module with masked convolutional layers and attention scoring further refines feature aggregation, ultimately enabling the prediction of epitope markers and antigen immunogenicity, thus guiding rational vaccine design. For researchers new to AI-driven vaccine development, this figure demonstrates clearly how combining multiple sources of biological data (sequence and structural features) through sophisticated attention-based models can enhance the accuracy and reliability of identifying promising vaccine antigen candidates, providing practical guidance for comprehensive, data-informed vaccine formulation. Q Query, K Key, V Value.

A key strength of VenusVaccine is its extensive dataset, comprising over 9500 antigens from diverse pathogens and cancer, each annotated experimentally as protective or non-protective. The model employed supervised learning (using binary cross-entropy loss), extensive data augmentation, and regularization to mitigate data imbalance and improve generalizability. VenusVaccine integrated pre-trained embeddings from protein language models (sequences) and structural embeddings (3D motif descriptors), employing a dual-attention mechanism fine-tuned to simultaneously optimize accuracy, AUC, and recall⁴⁹.

VenusVaccine achieved state-of-the-art performance, surpassing traditional methods by 5–10 percentage points in ROC-AUC and accuracy on independent benchmarks, with ROC-AUC values frequently exceeding 0.90. The model maintained high sensitivity and improved precision, effectively identifying true epitopes while reducing the number of false positives. Importantly, predictions were validated experimentally through literature-based confirmation of known and novel vaccine antigens, highlighting the practical utility of VenusVaccine’s multimodal, deep-learning-driven approach in vaccine target selection⁴⁹.

Performance benchmarking against established tools

Deep learning models for epitope prediction have significantly outperformed traditional methods such as NetMHCpan and MHCflurry. For example, MUNIS, trained on >650k peptide–HLA pairs, achieved a median average precision of 0.894 for HLA class I binders, surpassing NetMHCpan 4.1 (0.868) and MHCflurry 2.0 (0.867) on identical datasets. Models like MUNIS and BigMHC consistently show superior AUC and precision across diverse HLA alleles and peptides, demonstrating notable improvements (e.g., a 26% relative performance increase over MHCflurry). Importantly, these models also predict novel epitopes that were unseen during training; MUNIS successfully identified new Epstein–Barr virus CD8 T-cell epitopes that were validated experimentally, highlighting its capability to capture biologically relevant signals beyond memorizing known data¹⁹.

B-cell epitope predictors are commonly assessed using structural datasets with known antibody-binding sites. Recent deep learning models utilizing structural data have significantly outperformed traditional methods, such as BepiPred and Ellipro. For instance, GraphEPN, a GNN-based model, achieved higher AUC and AUPR than popular tools (SEMA 2.0, BepiPred 3.0, SEPPA 3.0), improving true epitope detection⁶¹. Similarly, GraphBepi, which integrates AlphaFold2 structures, boosted AUPR by approximately 44%, significantly reducing false positives⁴³. These improvements markedly enhance predictive accuracy. Additionally, AI-driven predictors, including NetMHCpan and DeepHLApan, have demonstrated practical utility in neoantigen identification, increasing precision in vaccine development pipelines⁶⁵. Overall, advanced AI models surpass classical methods in terms of accuracy, precision, and the discovery of novel epitopes that are validated experimentally.

Structural epitope prediction and 3D modeling

Integrating 3D structural data represents a significant advancement in epitope prediction, particularly for B-cell (antibody) epitopes. Linear B-cell epitopes (continuous peptide segments) constitute only ~10% of all B-cell epitopes²⁷. In contrast, most are structural epitopes formed by amino acids distant in sequence but brought together on the folded protein surface. Traditional sequence-based algorithms are unable to identify these conformational epitopes, making structure-based AI approaches crucial. Two key developments have enabled progress: the widespread availability of high-quality predicted structures (thanks to AlphaFold2) and novel deep learning architectures exploiting those structures.

AlphaFold-enabled predictions

The breakthrough of AlphaFold2 in protein folding enables us to obtain reliable 3D models for many antigens, even when no experimental structure is available. Researchers have quickly leveraged this by feeding AlphaFold models into epitope predictors. For example, the GraphBepi framework utilizes an antigen’s AlphaFold2 structure to construct a graph, ensuring that the spatial environment of every potential epitope residue is considered^43,66. This approach revealed that using predicted structures instead of actual crystal structures can improve epitope prediction accuracy, effectively broadening the scope of structural epitope prediction to virtually any protein antigen. One study noted that switching from AlphaFold2 to the newer AlphaFold3 improved the underlying structure quality. Still, the epitope model’s performance only modestly increased, suggesting diminishing returns and that current models already extract the most relevant structural signal^67,68. Nonetheless, incorporating structural context from predicted models is a game-changer: it was previously impossible to attempt conformational B-cell epitope mapping on, say, a newly emerging viral protein with no PDB structure, but now AI can do precisely that.

Graph neural networks and 3D features

As mentioned, GNNs are a natural choice for analyzing protein structures. Methods like EpiGraph (2024) used a graph attention network (GAT) on protein structure graphs, capturing the idea that epitope residues tend to cluster on surfaces²⁷. By exploiting the homophily principle (nearby nodes in the graph tend to share labels after passing), GNN models effectively learn that if several neighboring residues are identified as candidate epitopes, the connected region is likely to be an epitope patch. This was combined with pretrained protein embeddings (from transformers) to provide evolutionary context, yielding state-of-the-art results²⁷. Another innovative direction is the use of geometric deep learning on protein surfaces: instead of residue-level graphs, some models project surface patches into a learned space. For instance, a recent approach employed a variational autoencoder to encode local surface shapes and chemistry into discrete tokens, which then inform a graph transformer about likely epitope “hotspots”⁶¹. These approaches explicitly incorporate known correlates of epitope location, such as high solvent accessibility, protruding loops, or flexible regions, by adding these features to the graph or allowing the network to infer them from multiple examples.

T-cell epitope structure

While T-cell epitopes are linear peptides, structure can still play a role in prediction. Peptide-MHC binding involves a specific conformation, and some deep learning methods have started to incorporate structural modeling of the peptide–MHC complex. For example, structure-based scoring functions or docking predictions can be used in conjunction with sequence-based networks to enhance MHC binding predictions (this approach is akin to combining rapid sequence prediction with more detailed, yet slower, structural checks). Moreover, an emerging challenge, predicting TCR-epitope interactions, may require 3D modeling, as the orientation and contacts between a T cell receptor, peptide, and MHC are highly structural. Early deep learning efforts, such as TCR–epitope binding predictors, have utilized attention mechanisms to implicitly model these interactions⁶⁸ or even employed 3D convolutional networks on simulated complex structures⁶⁹. Such methods are still in their infancy but represent the frontier of structural immunoinformatics.

In summary, AI tools incorporating 3D structure, whether via graph networks, surface-learning, or multi-channel (sequence + structure) models, now outperform purely linear predictors for B-cell epitopes²⁷. They open the door to novel predictions (e.g., identifying an epitope on the surface of a viral spike protein that only forms when the protein trimerizes). The synergy of AlphaFold-provided structures with deep learning ensures that the availability of experimental maps no longer limits the prediction of structural epitopes.

Emerging trends and novel insights

Beyond the core advances above, several novel or under-discussed aspects of AI-driven epitope prediction are worth noting, as they promise to shape the next generation of vaccine design tools:

1. Neoepitope and Immunogenicity Prediction

   Traditional T-cell epitope tools have focused on peptide–MHC binding as a proxy for T-cell response; however, not all binders are immunogenic. Modern AI models address the challenge of predicting T-cell immunogenicity (i.e., whether a peptide will elicit a T-cell response). It is critical to recognize that immunogenicity alone does not guarantee protective or neutralizing immune responses. Indeed, highly immunogenic epitopes can sometimes be non-neutralizing, auto-reactive, or poly-reactive, posing risks of adverse immune reactions. Therefore, functional validation and safety assessments are essential when selecting epitopes for vaccine design. For instance, DeepHLApan introduced a two-module network: one predicts HLA–peptide binding and another predicts if that peptide–MHC complex will trigger T cells. By integrating these components, DeepHLApan significantly improved precision in identifying true neoantigens among many binders in cancer immunotherapy studies⁶⁵. Similarly, BigMHC and MUNIS utilized transfer learning on immunopeptidome data to rank peptides based on the likelihood of T-cell recognition, rather than just binding¹⁹. These approaches highlight previously underrecognized factors (such as TCR repertoire and antigen processing efficiency) and attempt to incorporate them into predictions, shifting from pure binding affinity to immune outcome prediction.

2. Generative Design of Epitopes

   An exciting emerging field is the use of AI to predict existing epitopes and generate new epitope sequences with desired properties. Deep generative models such as GANs and variational autoencoders are being applied to immunological data. In one example, DeepImmuno-GAN was trained to produce synthetic peptides that resemble known immunogenic epitopes; remarkably, the generated peptides exhibited physicochemical properties and predicted immunogenicity scores comparable to those of real epitopes³⁰. Such generated candidates can expand the search space for vaccine designers and even suggest modifications to known epitopes to increase their potency or cross-reactivity. This direction is relatively new and under-discussed in reviews. Still, it holds promise for computational vaccine design, where algorithms propose optimal epitope ensembles or novel variant epitopes that could broaden immune coverage.

Attention interpretability and epitope features

Deep learning models for epitope prediction increasingly incorporate interpretability methods such as attention weights, saliency maps, and Shapley additive explanations (SHAP), across convolutional, transformer, and graph neural network architectures. These techniques identify key features (such as residues, regions, or physicochemical patterns) that drive predictions, aligning computational outputs with biological understanding. For B-cell epitopes, graph-based models like GraphEPN visualize residue-level epitope scores on 3D antigen structures, revealing immunogenic “hotspots” that guide targeted experimental validation, such as site-directed mutagenesis⁶¹. Similarly, deep transfer-learning models have identified immunodominant regions within the SARS-CoV-2 spike protein, accurately highlighting known antibody-binding domains⁷⁰. Transformer-based sequence models also utilize internal attention and saliency mapping to identify critical sequence motifs, facilitating the experimental discovery and validation of novel antibody-binding epitopes, as demonstrated by Wang et al. using an explainable language model (mBLM)⁷¹.

In T-cell epitope prediction, interpretability helps align predictions with established immunological principles, enhancing acceptance among experimentalists. Attention mechanisms and attribution methods, such as SHAP, identify essential peptide residues or T-cell receptor (TCR) features critical for binding⁷². For peptide–MHC predictions, local explanations (e.g., LIME, SHAP) highlight known anchor residues, reinforcing confidence that predictions reflect biologically meaningful interactions rather than artifacts⁷³. This facilitates generation of testable hypotheses, guiding immunologists toward actionable experimental targets. Thus, interpretability approaches strengthen the bridge between computational models and biological validation, enhancing practical adoption of AI in immunology^61,71.

• c.Multi-epitope Vaccine Optimization

  AI is also used to solve combinatorial problems like selecting an optimal set of epitopes for a vaccine (covering broad population HLA diversity or multiple viral strains). Advanced algorithms can evaluate millions of epitope combinations, striking a balance between immunogenicity and coverage. Some in silico studies use deep learning predictors cascaded into an optimization framework (genetic algorithms, integer linear programming) to propose multi-epitope vaccine designs with maximal predicted population protection^74–76. While largely theoretical, these approaches foreshadow AI-driven vaccine formulation, an aspect not extensively covered in most epitope prediction reviews.

  An additional potential future application directly related to AI-driven epitope prediction tools is the optimization of multi-epitope vaccines. While still emerging and largely theoretical, these methods leverage deep-learning-predicted epitopes to systematically evaluate millions of possible epitope combinations, aiming to identify sets that maximize immunogenicity and population coverage. Although beyond the primary scope of epitope prediction, this integrative approach illustrates the future practical impact and utility of AI models in rational vaccine design workflows

Comparative AI epitope prediction tools

This review emphasizes functional attributes relevant for determining a tool’s suitability in a wet-lab setting, including accessibility (e.g., user-friendly web servers vs. standalone software), the required input data (primary sequences vs. 3D structural information), and the interpretability of outputs. All the tools discussed originate from peer-reviewed studies and report quantitative performance metrics (e.g., accuracy, AUC on benchmark datasets), facilitating an evidence-based selection process. By synthesizing these features, we aim to support the selection of practical and informed tools⁷⁷. Table 1 provides a quantitative benchmark of state-of-the-art AI-driven epitope prediction tools for both B-cell and T-cell epitopes. The table systematically compares tools across critical performance metrics, including AUC, accuracy, precision, recall, and Matthews’ correlation coefficient (MCC), drawn directly from primary research sources. These metrics facilitate evidence-based selection, clearly identifying methods that consistently outperform traditional approaches, especially transformer- and graph neural network-based models. Notably, tools that integrate structural data (e.g., GraphBepi, DiscoTope 3.0) exhibit significant improvements in predicting conformational B-cell epitopes. This rigorous comparison enables experimental immunologists to make informed, context-specific choices of tools, thereby bridging computational predictions with practical vaccine development workflows.

Table 1.

Comparison of AI-driven tools for B-cell and T-cell epitope prediction: architectures, reported performance metrics, and practical availability

Tool	Epitope Type	AI Architecture	Structural Input	Performance (Key Metrics)	Availability	References
MUNIS (2025)	T-cell (HLA-I immunogenic peptides)	Transformer (ESM-2) + BiLSTM hybrid	No	ROC–AUC ≈ 0.980 (median) and AP ≈ 0.289 on test (≈26% better than next best); identified known & novel CD8⁺ epitopes in EBV (exp. validated)	Code (Zenodo)	19
BigMHC (2023)	T-cell (MHC-I binding & neoepitope immunogenicity)	Ensemble of 7 deep NNs (incl. LSTM)	No	ROC–AUC = 0.9733, PR–AUC = 0.8779 for peptide–MHC ligand prediction; after transfer-learning, achieved higher precision than 7 prior models for neoepitope immunogenicity	GitHub	26
DeepImmuno (2021)	T-cell (peptide immunogenicity)	CNN (two-stream peptide+HLA)	No	Outperformed earlier tools (IEDB, DeepHLApan) with markedly higher precision and recall on viral and cancer epitope benchmarks (e.g., PR–AUC ≈ 0.62 on an independent set)	Web and GitHub	30
DeepHLApan (2019)	T-cell (MHC-I neoantigen binding + immunog.)	RNN-based multi-task (binding + immunogenicity)	No	Binding model AUC > 0.90 on 43 HLA alleles (comparable to NetMHCpan); adding immunogenicity filter boosted neoantigen precision by 43.8% (at top 10% rank) (e.g., ~9.6% → 13.8% PPV)	Web & GitHub	65
MHCnuggets (2020)	T-cell (MHC-I/II binding affinity)	LSTM network (pan-allelic)	No	AUROC ≈ 0.82, PPV_n ≈ 0.42 on ligand test (≥3× higher PPV than NetMHCpan4.0, MHCflurry, etc.); scanned 26 million peptide–HLA pairs in ~2.3 h (high-throughput)	GitHub (open-source)	37
MHCflurry (2018)	T-cell (MHC-I binding affinity)	ANN ensemble (allele-specific)	No	AUROC ≈ 0.92–0.93 on IEDB benchmarks (comparable to NetMHCpan); top-speed predictor (GPU-optimized) scanning millions peptides/sec	GitHub & API	110
NetMHCpan 4.1 (2020)	T-cell (MHC-I binding affinity)	ANN (pan-specific)	No	Improved class I binding prediction vs prior: e.g., AUROC ~ 0.925 on ligand benchmarks; widely used baseline (covers any HLA-I allele)	Web Server	111
NetMHCIIpan 4.0 (2020)	T-cell (MHC-II binding affinity)	ANN (pan-specific)	No	State-of-art for CD4⁺ epitopes until Transformers: e.g., AUROC ~ 0.877 (HLA-II binding); forms baseline for class II prediction	Web Server	111
BERTMHC (2021)	T-cell (MHC-II peptide binding)	Transformer (BERT fine-tune)	No	ROC–AUC ≈ 0.882 for class II binding (vs 0.877 by NetMHCIIpan); attention heads correctly highlighted key anchor residues	GitHub (model code)	51
DeepBCE (2023)	B-cell (linear epitope immunogenicity)	CNN (sequence-based)	No	Accuracy = 87.8%, AUC = 0.945, F1 = 0.871 on benchmark (10-fold CV); MCC ~ 0.59 higher than prior state-of-art on average	Web (PythonAnywhere)	18
DeepLBCEPred (2023)	B-cell (linear epitope)	BiLSTM + multi-scale CNN + attention	No	~66–67% accuracy, MCC ~ 0.35 on IEDB tests (slightly above previous ~0.34 MCC); on a larger independent set, outperformed BepiPred2.0 by +0.31 ACC and +0.63 MCC (substantially higher precision)	Standalone (Front. Microbiol)	35
BepiPred 3.0 (2022)	B-cell (linear & conformational)	Transformer (ESM-2 embeddings + ANN)	No	ROC–AUC ≈ 0.70–0.71 on conformational epitope test (vs ~0.58 by BepiPred2.0/epitope3D); best overall in 2022 benchmark of structure-based tools	Web Server (DTU)	77
EpiDope (2021)	B-cell (linear epitope)	BiLSTM (with dense layers)	No	ROC–AUC ≈ 0.67 ( ± 0.07) in 5-fold CV, significantly above earlier methods (~0.56 AUC); improved detection of known linear epitopes in tests	GitHub (source code)	112
SEMA 2.0 (2024)	B-cell (conformational epitope)	Ensemble AI (sequence + 3D features)	Yes	Reported ROC–AUC ≈ 0.75–0.80 on structure-based epitope benchmarks (vs ~0.66 by older tools); integrates AI for sequence & surface patch analysis (NAR test winner)	Web Platform	79
epitope3D (2022)	B-cell (conformational epitope)	Gradient boosting (graph features)	Yes	ROC–AUC ≈ 0.59 on external test (vs ~0.51 DiscoTope2.0); one of first ML approaches combining structural graph signatures with ML classifier	GitHub (Python)	113
DiscoTope 3.0 (2024)	B-cell (conformational epitope)	Inverse-folding transformer + CNN	Yes	AUC–PR ≈ 0.232 on benchmark (vs 0.177 by next-best BepiPred3.0); top F1 and MCC among structure-based predictors in 2024 study	Web Server	114
GraphBepi (2023)	B-cell (conformational epitope)	GNN (graph attention) + BiLSTM	Yes	ROC–AUC improved by >5.5% and AUC_PR by ~44% over prior SOTA (e.g., ~0.80 vs 0.75 AUC; PR AUC ~ 0.24 vs 0.17); leverages AlphaFold2 structures for +5–6% absolute gain in accuracy	Code (available in the article)	43
EpiGraph (2024)	B-cell (conformational epitope)	GNN (graph attention) + LM embeddings	Yes	AUC_PR ≈ 0.23–0.24, outperforming BepiPred3.0/DiscoTope on benchmark (vs ~0.19); exploits spatial epitope clustering (graph homophily) to boost precision	(research prototype)	27
GraphEPN (2025)	B-cell (conformational epitope)	VQ-VAE + Graph Transformer (GNN)	Yes	Achieved highest accuracy and MCC on multiple benchmarks (significantly outperforming SEMA2.0, BepiPred3.0, etc.); robust across diverse antigens (uses pretrained structural embeddings)	(research prototype)	61

T-cell epitopes include MHC–peptide binders and immunogenic peptides recognized by T cells, whereas B-cell epitopes include linear peptide segments or conformational surface patches on antigens. Performance metrics like ROC–AUC (area under the ROC curve) and AUC_PR (area under the precision–recall curve, a.k.a. average precision) summarize overall classification performance. “Structural Input” indicates whether the method utilizes 3D structural information (e.g., an antigen’s tertiary structure) in its predictions. All performance values above are as reported in each method’s primary publication; they were obtained on differing evaluation datasets or splits. No unified benchmark was performed in this work, so these numbers provide a helpful overview but are not directly comparable across tools.

ACC accuracy, MCC Matthews correlation coefficient, PPV positive predictive value.

Notably, the tools compared here utilize diverse AI architectures and data modalities, which impact their optimal use cases. Some predictors rely solely on protein sequence inputs, utilizing advanced neural network models (ranging from early recurrent networks to modern transformer-based language models) to identify linear B-cell epitopes or MHC-binding peptides^77,78. These sequence-focused methods are well-suited for tasks such as scanning pathogen genomes for candidate peptide vaccines or predicting T-cell epitopes, where only the primary structure is available. In contrast, other tools incorporate tertiary structure data (experimental or AlphaFold-predicted) to improve the prediction of conformational B-cell epitopes recognized by antibodies^78,79. Such structure-enabled approaches often employ geometric deep learning or graph-based networks to pinpoint discontinuous surface epitopes⁴³. These make them invaluable for antibody epitope mapping when an antigen’s 3D context is known. Different output formats further influence interpretability: many platforms provide residue-level epitope scores that can be visualized on protein sequences or structures for intuitive interpretation. For example, one tool’s web server displays color-coded antigen residues (with high-scoring regions highlighted) on the protein sequence and 3D structure, directly indicating predicted epitope locations⁷⁹. This level of interpretability is critical for wet-lab researchers, as it allows direct mapping of AI predictions to experimental validation plans. The architectural and input distinctions among these AI tools translate into different strengths: an immunologist targeting antibody epitope mapping on a complex antigen may favor a structure-informed model, whereas one designing a peptide-based vaccine might choose a high-throughput sequence-based predictor. The comparative metrics and features compiled in the table below facilitate such decisions, ensuring that researchers select the most appropriate AI tool for their specific experimental objectives while understanding each tool’s requirements and evidentiary support⁷⁹.

Practical integration of AI predictions into experimental vaccine design

Experimental validation strategies

AI-driven epitope predictions require rigorous wet-lab validation to confirm immunological relevance. For T-cell epitopes, initial validation involves confirming peptide-MHC binding through assays such as competitive ELISA or fluorescence-based tests with purified MHC molecules⁸⁰. These assays measure peptide affinity (IC₅₀), validating model predictions. However, binding alone is insufficient; functional T-cell activation must also be assessed using assays like IFN-γ ELISpot or intracellular cytokine staining (ICS), which measure T-cell responses directly⁸¹. ELISpot quantifies cytokine-producing T cells in response to peptide stimulation, identifying truly immunogenic peptides. For instance, a study on a SARS-CoV-2 spike vaccine confirmed substantial T-cell responses to 4 out of 10 predicted epitopes⁸¹. ICS further characterizes responding T-cells and their cytokine profiles. Combining MHC binding assays with functional T-cell validation ensures robust selection of epitopes for vaccine development.

For B-cell epitopes, validation strategies vary depending on the epitope type. Linear epitopes (continuous peptides) are validated through antibody-binding assays such as ELISA, where synthetic peptides coated on plates capture specific antibodies from sera^82,83. Conformational epitopes, which involve discontinuous regions of a native protein surface, require structural validation. AI-driven predictions (e.g., GraphBepi) identify these epitopes from protein structures. Experimental validation typically involves whole proteins or protein domains, employing competition assays or site-directed mutagenesis. Mutagenesis at predicted residues, followed by measuring antibody binding reduction, confirms epitope identity⁸⁴. High-resolution methods such as X-ray crystallography or cryo-electron microscopy precisely map antibody-antigen interfaces, providing unambiguous epitope validation⁸⁴. Thus, aligning experimental methods (peptide-centric for linear epitopes and structurally preserved assays for conformational epitopes) with AI predictions enables the efficient confirmation of epitopes relevant for vaccine development.

Case studies with quantitative outcomes

Real-world studies have begun to validate AI-predicted epitopes in settings related to infectious diseases and cancer, providing insights into the accuracy and limitations of these predictions. For example, Titov et al. evaluated 118 AI-selected SARS-CoV-2 T-cell epitopes across diverse human cohorts⁸⁵. Of these, approximately 63% (75/118) elicited measurable T-cell responses ex vivo, with 24 peptides emerging as broadly immunodominant. Excluding cross-reactive epitopes, the authors identified 73 true SARS-CoV-2 epitopes, achieving 95% diagnostic accuracy for infection or vaccination status⁸⁵. This illustrates that although AI predictions can efficiently identify immunogenic epitopes, careful experimental validation remains essential to filter false positives and confirm true targets⁸¹.

AI-guided epitope design has shown promising results beyond the SARS-CoV-2 virus. A recent study using a deep-learning approach identified conserved T-cell epitopes from the SARS-CoV-2 genome, formulated them into a DNA vaccine, and tested it in mice. Remarkably, 15 of 17 predicted epitopes elicited measurable T-cell responses and protected vaccinated mice from lethal viral challenge, even in the absence of neutralizing antibodies⁸⁶. However, success rates vary significantly depending on pathogen, epitope prediction methods, and experimental conditions. For instance, Sakabe et al. experimentally validated computational predictions of Ebola virus CD8+ epitopes using HLA-transgenic mice and found that only approximately 30–50% of the predicted peptides elicited measurable interferon-γ T-cell responses, indicating that factors such as immunological context, epitope processing, vaccine delivery method, and host genetics substantially impact the actual in vivo immunogenicity of predicted epitopes^87–89.

In cancer immunotherapy, AI-driven predictions of neoantigens have shown varied success, underscoring the critical need for rigorous experimental validation. High-throughput studies indicate that only about 6–32% of top-ranked neoantigens predicted to bind HLA-A*02:01 elicit measurable T-cell responses^90,91. However, clinical trials involving neoantigen-based vaccines have reported more favorable outcomes. For instance, a poly-neoantigen vaccine in melanoma patients successfully induced T-cell responses against over 60% of the peptides included. Similarly, an mRNA neoantigen vaccine targeting pancreatic cancer triggered robust T-cell responses against the majority of the predicted epitopes, correlating positively with delayed tumor relapse^91,92. While SARS-CoV-2 has frequently served as a prominent example demonstrating the power of AI-driven epitope prediction, particularly through the rapid identification of critical epitopes in the virus’s Spike protein, other challenging pathogens such as malaria, influenza, and HIV have also significantly benefited from these approaches^93–95. Recent applications of AI in malaria research have successfully identified novel antigens and epitopes from Plasmodium falciparum that can elicit robust immune responses, thereby providing new insights into the selection of vaccine targets⁹⁶. Likewise, AI-based approaches in influenza research have effectively identified highly conserved, broadly neutralizing epitopes, guiding efforts toward the development of universal influenza vaccine candidates⁹⁷. In the context of HIV, deep learning techniques have uncovered previously overlooked, conserved epitopes that are crucial for designing vaccines intended to elicit broadly neutralizing antibodies⁹⁸. Collectively, these examples highlight the broad applicability and substantial translational potential of AI methods in tackling diverse infectious diseases beyond SARS-CoV-2.

Targeted experimental validation strategies, such as co-culturing patient-derived T cells with computationally predicted peptides, further confirm the immunogenicity and therapeutic relevance of AI-identified neoantigens⁹⁹. Despite the proven ability of AI-based methods to discover epitopes missed by traditional methods, documented cases of non-canonical epitopes directly translating into successful vaccine formulations remain limited. Established vaccines continue to rely primarily on well-characterized, immunodominant epitopes. Nevertheless, recent case studies, such as AI-driven identification of conserved but previously overlooked epitopes in SARS-CoV-2 and Epstein–Barr virus, underscore the potential translational impact of these novel discoveries. Moving forward, an increased focus on prospective validations and clinical trials that explicitly assess these novel epitopes is essential to fully realize and substantiate the translational benefits offered by AI-driven epitope prediction. However, discrepancies remain instructive; for instance, a recent AI-guided SARS-CoV-2 antibody selection effort inadvertently identified antibodies targeting overlapping epitopes, resulting in diminished neutralization effectiveness⁸⁴. Such outcomes emphasize the importance of iterative feedback loops between computational predictions and rigorous experimental validations, ultimately enhancing the precision and efficacy of epitope selection in therapeutic vaccine development.

Best practices for interpreting and prioritizing predictions

Given the deluge of candidate epitopes that AI tools can produce, experimental immunologists need clear criteria to triage which predictions to pursue. The first best practice is to apply stringent scoring thresholds while being aware of the trade-offs in recall. Epitope prediction algorithms output measures such as binding affinity (IC₅₀ in nM) or percentile rank. It is common to use cut-offs such as IC₅₀ < 500 nM (for MHC binders) or top 1–2% rank to define “strong” candidates. For example, neoantigen studies often retain only mutations yielding peptides predicted to bind HLA with <500 nM affinity and that are expressed in the tumor (RNA reads ≥1)⁹⁹. Imposing such cut-offs focuses resources on high-likelihood epitopes. However, stringent thresholds can sacrifice sensitivity; one analysis noted that “commonly used” affinity cut-offs captured only ~40% of true binders in an empirical dataset. To improve recall, practitioners may lower the threshold or use an optimal balance point determined by benchmarking data⁸⁰. In practice, a multi-tier approach is effective: start with a relatively lenient filter (e.g., 500 nM or the top 5% rank) to gather a pool of candidates, then prioritize within this pool using additional factors as described below. This avoids prematurely discarding peptides that might be biologically important (for instance, some true CD4⁺ epitopes bind MHC-II with moderate affinities above the typical cut-off)¹⁰⁰. It’s also wise to remember that prediction scores are not absolute; consider the margin between candidates. A peptide with a predicted 50 nM affinity is potent, but a 505 nM affinity peptide might not be worse than one at 495 nM; thus, rigid thresholds should not override expert judgment if other evidence favors a slightly sub-threshold epitope¹⁰¹.

Consensus and cross-validation among different epitope prediction tools increase confidence in selected candidates. Because predictions from various algorithms (e.g., NetMHCpan vs. motif-based methods for T cells, or multiple B-cell predictors) often partially overlap, epitopes ranking highly across independent methods are more reliable. For instance, a SARS-CoV-2 multi-epitope vaccine design combined sequence-based (NetCTLpan) and structure-based predictions to select robust candidates. However, relying solely on consensus can omit unique epitopes detected by individual tools. Therefore, a balanced ensemble voting approach that prioritizes epitopes predicted by multiple tools, while also including some top-scoring unique predictions, can enhance coverage.

Integrating biological context further improves prediction relevance, as AI models often overlook factors such as antigen abundance, processing pathways, or immune tolerance. For example, prioritizing epitopes from highly expressed proteins or conserved pathogen regions enhances in vivo relevance⁹⁹. For tumor neoantigens, confirming mutation exclusivity to cancer cells prevents tolerance issues. Consulting existing immunological data, such as the Immune Epitope Database (IEDB), provides validation and aids assay design by identifying historically immunogenic motifs. Additionally, incorporating prediction confidence metrics or uncertainty estimates (e.g., confidence intervals or ensemble variance) helps rank epitopes more reliably, balancing score and consistency⁸⁰. By integrating quantitative thresholds, tool consensus, and biological context, researchers effectively refine AI-generated epitope lists into experimentally actionable candidates.

Integrating AI tools into current laboratory workflows

Integrating AI epitope prediction into vaccine laboratories requires coordination between computational analyses and experimental work. Researchers must first ensure access to suitable computational resources. Many sequence-based predictors (e.g., NetMHCpan) can run efficiently on standard lab computers and are accessible through user-friendly interfaces⁸⁰. Conversely, advanced methods using structure prediction or deep-learning models (e.g., GraphBepi or AlphaFold2) typically require powerful GPU hardware or cloud-based solutions^43,102. Computational times for such models can range from hours to days, especially when processing large antigen libraries.

Careful data preparation is also essential. Experimentalists must precisely format antigen sequences and structures according to software requirements, trimming unnecessary regions, adding allele-specific information for MHC tools, and converting structural files (e.g., PDB, mmCIF). Minor formatting mistakes can disrupt analyses or produce incorrect outputs. Initial testing with well-characterized antigens can help familiarize users with the tools. While web-based interfaces (e.g., IEDB, ImmuneAI) simplify predictions without local installations, users should consider data privacy issues and submission limits

Software literacy and team collaboration are essential for effectively integrating AI epitope prediction into vaccine design workflows. Rather than expecting all immunology researchers to become programming experts, it’s beneficial to foster collaboration between experimentalists and computational biologists or bioinformaticians. An interdisciplinary team member, such as a graduate student or postdoc, can bridge these fields by running AI analyses, automating data parsing into user-friendly formats (e.g., annotated Excel sheets), and maintaining code reproducibility through version control. Providing experimentalists with basic training in command-line tools or FASTA file handling can also enhance workflow integration. Regular joint meetings allow computational and experimental members to review and select predictions collaboratively, ensuring transparency and informed decision-making. To address common bottlenecks, such as the overwhelming volume of AI-generated predictions, labs can adopt phased validation, initially testing a manageable subset of top-ranked epitopes before expanding. Early reagent planning is crucial, as peptide synthesis and quality control can be both costly and time-consuming. Using peptide pools in ELISpot assays accelerates throughput, though careful informatics support is required for deconvolution. Integrating automated systems (e.g., ELISA plate readers, barcode labeling) further enhances efficiency and facilitates seamless transition from computational predictions to experimental validation⁸⁵.

Effective integration of AI into vaccine workflows relies heavily on clear communication. Experimental teams should clearly outline practical constraints (e.g., the number of peptides that can be tested per quarter) to computational colleagues, who can then adjust the prediction criteria accordingly. Conversely, computational scientists should clarify prediction scores and associated uncertainties, providing experimentalists with realistic expectations of success rates. By embedding AI as an iterative and collaborative element within experimental pipelines, where wet-lab results continually refine computational models, researchers transition from a trial-and-error approach to a more efficient, data-driven method. Recent studies highlight how this integration accelerates epitope discovery for next-generation vaccines^86,103. Ultimately, AI serves as a valuable guide, rather than a replacement for laboratory work, enhancing precision and efficiency in antigen selection through thoughtful integration and rigorous validation.

Current challenges and future directions

Despite the impressive progress, there remain significant challenges for AI-based epitope prediction that researchers are actively working to address:

1. Data Limitations and Bias

   Deep learning performance is tightly linked to the quantity and quality of training data. Epitope datasets are still limited and biased; for example, specific pathogens (such as influenza and SARS-CoV-2) or common HLA alleles are overrepresented, while other diseases or rare alleles have few examples. Negative data (true non-epitopes) are particularly scarce, since most “non-epitope” labels are inferred (lack of experimental evidence) rather than definitively tested. This can skew models to over-predict epitopes. Efforts such as data augmentation (including the generation of synthetic negatives or decoy peptides) and semi-supervised learning are being explored to mitigate this issue^104,105. Transfer learning from general protein datasets (as done with pretrained language models) also helps infuse general biochemical knowledge into models when epitope-specific data are sparse⁵¹.

2. Generalizability to Novel Pathogens

   A model may perform well on known pathogens but falter on a new one. Generalization across distant species or rapidly mutating viruses remains difficult. For example, a network trained primarily on viral proteins might struggle with parasitic worm epitopes that have different immunological signatures. Similarly, predicting epitopes for emerging viral variants (with mutations) tests a model’s ability to extrapolate. Cross-validation studies (training on one pathogen type and testing on another) reveal that some models degrade in performance, highlighting the need for more robust features. Continual learning and updating models with new experimental data (as it becomes available for new pathogens) is a likely solution. Models like VenusVaccine aim to enhance cross-species generalizability by incorporating diverse antigen types into training; however, this remains an active area of research⁴⁹.

3. Integration of Immune Context

   Current predictors often treat epitopes in isolation. However, in a biological context, an epitope’s immunogenicity can depend on factors such as its flanking sequence (for processing), the pathogen’s expression level, or host factors (HLA repertoire, TCR repertoire, and B-cell lineage availability). Contextual data integration remains an open challenge, for example, incorporating antigen processing predictors (for T-cells) or host genomic data. Recent work integrates proteasome cleavage and TAP transport predictions into end-to-end models^65,100. Furthermore, structural flexibility, particularly of TCR loops, is a significant confounding factor for single-structure-based predictors, underscoring the importance of incorporating conformational variability to improve prediction accuracy and generalizability¹⁰⁶. In the future, epitope prediction might be just one module in a larger AI framework that models the entire immune presentation and response pathway.

4. Evaluation and Validation

   There is a need for standardized benchmarks and experimental validation pipelines. Different studies employ various metrics (e.g., AUC, AUPR, F1) and datasets, making direct comparisons challenging. Critically, a recent comprehensive benchmark by Cia et al. highlighted that many current epitope prediction methods significantly suffer from overfitting to their training datasets, drastically limiting their generalizability and predictive performance in real-world scenarios¹⁰⁴. Community-led benchmark datasets (e.g., IEDB curated sets) help, but models sometimes inadvertently overfit public benchmarks. A challenge is designing blind, prospective evaluations, for instance, predicting epitopes for a pathogen before experimental results are known, as in some COVID-19 studies, to truly test model generality. Moreover, translating prediction improvements into vaccine success is non-trivial; a top-ranked epitope might bind antibodies in vitro but could be poorly immunogenic in vivo¹⁰⁷. Therefore, closing the loop with experimental vaccine trials remains critical^15,80. Encouragingly, some AI-predicted epitopes have now advanced to animal studies and early clinical trials (especially in cancer neoantigen vaccines), but more such validation will solidify confidence in these tools. Additionally, accurately predicting the structure of short linear peptides remains challenging, particularly in pMHC contexts. Recent benchmarks demonstrate that models such as AlphaFold rely heavily on overall antigen homology, frequently generating conformations that deviate significantly from the native structures observed experimentally when peptides differ substantially from those in their training datasets. This limitation represents a major barrier to achieving generalizable T-cell epitope prediction methods and underscores the importance of enhancing model training with diverse structural data^68,108,109.

   Overall, AI has infused epitope prediction with new capabilities, from deep sequence understanding with transformers to structural insight with graph networks and has led to tangible improvements in identifying vaccine targets. The field is rapidly evolving, with hybrid models and novel training strategies pushing the envelope of accuracy. By addressing challenges such as data bias and context integration, future AI models could become even more reliable, potentially even to the point of designing entire vaccine formulations in silico. Such advances would dramatically accelerate the pipeline from pathogen discovery to vaccine development, fulfilling the promise of reverse vaccinology in the age of deep learning. The ongoing convergence of immunology, structural biology, and AI thus stands as a highly promising frontier in preventative medicine^19,49.

Conclusions

Integrating AI into vaccine development significantly accelerates antigen discovery, enhances predictive accuracy, and optimizes experimental workflows. Advanced AI methodologies, such as CNNs, recurrent neural networks, transformer-based models, and graph neural networks, have consistently outperformed traditional epitope prediction tools in terms of precision and recall. Specifically, deep learning models like MUNIS and GraphBepi have demonstrated superior performance in identifying novel immunogenic epitopes, which have been rigorously validated in experimental settings, highlighting their substantial translational potential.

Despite these advancements, key challenges remain. Insufficient and biased datasets limited predictive generalizability across diverse pathogens, and the critical need for rigorous experimental validations continues to constrain AI-driven epitope prediction. Future progress will depend heavily on data augmentation techniques, enhanced model interpretability, and standardized benchmarking frameworks.

This review systematically compares leading AI-driven epitope prediction tools through quantitative benchmarks, highlighting their practical strengths and limitations. Furthermore, we provide clear, actionable guidelines for integrating computational predictions into existing experimental protocols, emphasizing the importance of biological interpretability and real-world applicability. By closely coupling computational insights with empirical validation, our work supports vaccine researchers in effectively adopting AI technologies, ultimately fostering more efficient, accurate, and rational vaccine design to address global infectious disease threats.

Author contributions

F.V.F.: Conceptualization, writing and editing the original draft, funding acquisition, formal analysis, and methodology. J.I.S.-V.: Conceptualization, investigation, formal analysis, methodology, and editing of the original draft. I.G.A.: Conceptualization, investigation, formal analysis, methodology, writing, and editing the original draft.

Data availability

No datasets were generated or analysed during the current study.

Competing interests

The authors declare no competing interests.