Machine Learning in Immunology: From Epitope Prediction to Smarter Vaccine Design

By Soham NandiReviewed by Bethan DaviesApr 2 2025

Tracking how well vaccines work is more important than ever—especially with diseases like COVID-19 that keep evolving. But measuring vaccine efficacy isn’t always straightforward. Traditional methods rely heavily on clinical trials, which are time-consuming, resource-intensive, and often struggle to keep pace with real-world complexity: new variants, varying immunity levels, and inconsistent data quality.

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This is where machine learning (ML) comes in. By analyzing large, messy datasets far more efficiently than manual approaches, ML is helping researchers predict immune responses, optimize vaccine design, and adapt public health strategies in real time. From clinical studies to outbreak response, ML is changing the way we think about vaccine development.^3,4

Machine learning isn’t just a buzzword in healthcare anymore—it’s become a powerful tool that’s changing how we understand, diagnose, and treat disease. With the ability to analyze enormous, complex datasets, ML is helping researchers and clinicians uncover patterns that would be nearly impossible to detect otherwise. Whether it’s improving diagnostics, personalizing treatment, accelerating drug discovery, or predicting health risks, machine learning is playing a growing role in almost every corner of medical research.

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The Limitations of Traditional Vaccine Evaluation

While clinical trials have long been the gold standard for evaluating vaccine efficacy, they come with a number of well-known challenges—especially when speed and adaptability are crucial. These trials take time—often years—to recruit participants, administer doses, monitor immune responses, and collect outcome data. On top of that, the financial burden is substantial, with costs spanning everything from staffing and infrastructure to regulatory compliance and long-term follow-up.

Beyond time and cost, traditional trials are inherently complex. They require strict protocols, face significant ethical considerations, and demand careful coordination across large, often diverse participant groups. As modern vaccine research increasingly incorporates genetic data, immune profiling, and environmental variables, the volume and complexity of the data generated has grown significantly. This kind of high-dimensional data is difficult to interpret using conventional statistical tools alone.

Population variability adds another layer of complexity. Differences in age, pre-existing health conditions, and genetic background all influence how individuals respond to vaccination—making it harder to draw broad conclusions from trial results. And perhaps the most pressing challenge today: the rapid emergence of new variants. These can reduce the effectiveness of previously successful vaccines, requiring scientists to pivot quickly—sometimes rendering past trial data less relevant.

All of this underscores the need for new tools and approaches that can work alongside traditional trials. Machine learning offers a way to make sense of this complexity—helping researchers assess efficacy faster, more accurately, and with fewer resource constraints.

How Machine Learning Addresses These Gaps

As we’ve seen, traditional vaccine evaluation methods—while essential—struggle to keep pace with the complexity and urgency of modern public health needs. This is where machine learning steps in. By analyzing large, often messy datasets with speed and precision, ML helps fill critical gaps left by conventional approaches.

One of ML’s biggest strengths is its ability to recognize patterns and uncover insights that would be difficult, if not impossible, to detect manually. Vaccine research generates incredibly complex data—antibody profiles, genetic information, clinical outcomes, and more. ML algorithms can sift through this high-dimensional landscape to pinpoint the specific immune responses that correlate with protection. This not only helps us understand why a vaccine works but also guides the design of new ones that can trigger those same protective responses.

Machine learning also adds a layer of adaptability that’s hard to achieve through traditional methods. It can continuously analyze data from a wide range of sources—like electronic health records, genomic databases, even public health surveillance and social media—to detect emerging trends in real time. That kind of insight helps public health teams pivot quickly, whether it’s adjusting vaccine formulations or refining strategies for different populations and regions.

In short, ML allows researchers and policymakers to respond faster and more intelligently to shifting conditions. From designing more effective vaccines to deploying them more strategically, machine learning is helping make immunization efforts not only faster, but smarter and more equitable.

To understand how these capabilities play out in practice, it's helpful to look at how specific types of ML—like supervised and unsupervised learning—are being used to uncover immune patterns and predict vaccine efficacy.

Machine Learning in Immunology

Machine learning is making serious inroads in immunology, and a big part of that progress comes from the growing availability of large-scale datasets. Thanks to specialized databases, we now have access to detailed information about the main players in immune responses—antibodies, T cell receptors (TCRs), and antigens. These are often represented by their sequences and, in many cases, their 3D structures.

With all this data in hand, machine learning can do what it does best: spot patterns. By analyzing complex datasets, ML models learn to recognize structural or functional features that matter. For example, a model might learn to predict whether a certain protein sequence is likely to bind to a specific antigen. Training these models involves tweaking their internal settings over many iterations to perform better at a specific task—usually framed as optimizing an objective function. Once trained, the model can be used to make predictions on new, unseen data—all without having to run a lab experiment.

There are a couple of different ways to train these models. Supervised learning is used when we have labeled data—for example, to classify whether a protein site is part of an epitope, or to predict how strongly an antibody will bind to an antigen. On the flip side, unsupervised learning is useful when we don’t have labels and just want to explore the data—for instance, by clustering TCR sequences that seem to share similar binding features. This blend of prediction and discovery is what makes ML especially powerful in immunology.

In practical applications—like epitope discovery, designing immunogens, or predicting how epitopes and paratopes interact—ML models often output scores. These might represent the likelihood that a residue belongs to an epitope, or how likely a peptide is to be presented and trigger an immune response. These scores are incredibly useful because they help researchers quickly rank candidates and focus on the most promising ones. They also speed up later steps, like running molecular dynamics simulations, which can be time-consuming.

Different ML architectures come into play depending on the task. Whether you're doing classification, regression, or clustering, the choice of model matters. Some are great at capturing complicated, nonlinear relationships—like transformers—while others, like convolutional neural networks, may offer a better balance of performance and interpretability. The best fit depends on the question you're asking and the size and type of your dataset. Keep in mind that bigger models can handle more complexity, but they also run the risk of overfitting if your dataset isn’t large enough.⁷

Making Machine Learning Models More Interpretable

As machine learning takes on a bigger role in immunology, it’s not just about making accurate predictions anymore. Researchers want to understand why a model made a certain call—especially when it comes to things like why a particular epitope triggers an immune response, or how exactly an antibody binds to its target.

That’s where interpretability comes in.⁷

There are now several ways to make ML models more transparent and biologically meaningful:

• Feature importance tools, like decision trees, help highlight which sequence traits (like charge or hydrophobicity) are driving the model’s decisions.
• Model-agnostic methods like anchors explain predictions using simple if-then rules based on specific amino acid patterns.
• Attention maps from transformer models reveal which parts of a sequence the model focuses on—often the same spots involved in real binding interactions.
• Weight visualization in smaller models lets researchers actually see alternative binding motifs that the model has learned.
• Geometric deep learning goes a step further, surfacing spatial and chemical patterns—like an exposed, charged residue near a disulfide bond—that can explain why a site might be a good antibody target.

Some of these tools are specific to the architecture used (like attention in transformers), while others can work across different models. But in every case, combining these strategies with domain expertise is key to making sense of the biological relevance.

All of this points to a growing focus on not just building powerful ML models, but making sure they tell us something useful about the biology underneath.

How Machine Learning is Being Used in Real-World Immunology Studies

Machine learning isn’t just a behind-the-scenes tool—it’s already driving real progress in vaccine research and immune profiling. From predicting who’s protected after vaccination to uncovering unexpected immune patterns, here’s how ML is being applied in practice:⁷

1. Predicting Who’s Protected (Supervised Learning)

In one study on the PfSPZ-CVac malaria vaccine, researchers used supervised learning to find out which antibodies were actually linked to protection. They worked with massive amounts of data—immune responses covering nearly the entire Plasmodium falciparum proteome—and tested three types of ML models: logistic regression, random forests, and a multitask support vector machine (SVM).

The multitask SVM stood out. It was able to factor in time and dose response together, which made it especially good at handling complex, high-dimensional data—something that’s common in proteomics. To better understand the model’s predictions, the team developed a custom method called ESPY. It helped identify specific antigens (like CSP and PfEMP1) whose antibody patterns were strongly tied to protection. Even when overlapping features were removed, the SVM still performed well—something the other models couldn’t manage. This shows how ML can not only make accurate predictions but also highlight biologically meaningful markers that might guide future vaccine design.⁸

2. Uncovering Hidden Patterns: Unsupervised Learning and Infant Immunity

At Pwani University, researchers used unsupervised learning to explore how congenital infections—like CMV, HSV, and Toxoplasma gondii—affect how infants respond to vaccines. With no labeled data to guide the analysis, they turned to clustering methods like k-means to group infants by their antibody response profiles.

The patterns they found were striking. Infants exposed to certain infections at birth had noticeably different immune trajectories, suggesting that early-life exposure might rewire how their immune systems respond down the line. Follow-up analysis with other ML models, including neural networks and random forests, backed up these findings. Traditional methods would’ve missed these shifts, especially in such messy, longitudinal data. ML helped make sense of the noise and uncover insights that could have real implications for pediatric vaccine strategies.⁹

3. ML Meets Vaccine Design: From Adjuvant Effects to Epitope Discovery

Machine learning is also making a real difference in how vaccines are designed, optimized, and evaluated—especially in figuring out how different components of a vaccine influence the immune system.

Take adjuvants, for example. These are the ingredients added to vaccines to boost the immune response, and while they often contain similar core molecules, their formulation can significantly change how the body reacts. In one malaria vaccine study, researchers compared two adjuvants—AS01B and AS02A—which both include the same immunostimulatory components (QS21 and MPL), but in slightly different delivery systems.

Using random forest models to analyze immune profiling data from individuals with no prior malaria exposure, researchers examined around 40 immune features, including antibody levels, T-cell subtypes, and cytokine activity. What they found was unexpected: AS02A triggered a stronger CD8+ T cell and TFH17 (T follicular helper) response, while AS01B promoted a more immediate antibody surge. This kind of nuanced difference might have gone unnoticed using traditional statistical tools—but ML made it clear that formulation alone can shift the immune response in important ways.

This has major implications not just for malaria vaccines, but for any scenario where fine-tuning the type of immune response—cellular vs. humoral, early vs. sustained—could affect protection or safety. And because these insights were data-driven, they help push back on assumptions and open the door to rethinking how adjuvants are selected in future vaccine trials.¹⁰

ML is also playing a big role in sieve analysis, a method used to figure out how genetic variations in pathogens affect vaccine efficacy. In diseases like HIV and malaria, where pathogens are highly variable, it’s crucial to know whether a vaccine protects against certain strains better than others. Traditional sieve methods often struggled to account for all the variables involved. But ensemble ML models, which combine predictions from multiple algorithms, doubled the precision of strain-specific detection. This means researchers can now get a clearer picture of how a vaccine performs across diverse genetic backgrounds—and better evidence to support targeted vaccine updates.¹¹

On the design front, machine learning is helping to speed up epitope prediction, a key step in identifying which parts of a pathogen the immune system is most likely to recognize. Rather than relying solely on expensive and time-consuming lab screenings, ML models can now scan entire pathogen genomes and rank potential B- and T-cell targets based on predicted immunogenicity.

These predictive tools are already proving useful across a range of pathogens—from SARS-CoV-2 to bacterial infections to malaria. In some cases, they’ve even uncovered new immune correlates of protection. One example: ML models linked a specific subclass of antibodies, IgG4, to phagocytic activity in individuals who received the RTS,S malaria vaccine—a relationship that hadn’t been obvious before and could inform future vaccine refinements.¹²

The big takeaway here is that ML isn’t just speeding up discovery—it’s helping researchers ask better questions, spot unexpected patterns, and make smarter design choices, all while working with the kind of complex, high-dimensional data that traditional methods struggle to handle. But as promising as these tools are, applying ML in immunology isn’t without its challenges.

Limitations, Challenges, and Perspectives

So far, we’ve seen how machine learning is helping accelerate epitope discovery, predict immune responses, and support vaccine design. But as powerful as these tools are, their full potential hasn’t been reached yet—and a big reason for that comes down to some fundamental challenges in both the data and the methods.⁷

1. Data: The Biggest Bottleneck

No matter how advanced your ML model is, its output is only as good as the data it learns from. And in immunology, data limitations are still a major roadblock.

One of the toughest issues is thelack of high-quality, diverse, and representative training data, especially for modeling epitope-paratope interactions. These interactions—whether between antibodies and antigens or T cell receptors and peptide-MHC complexes—are incredibly diverse. You’re dealing with massive sequence space, conformational variability, and widespread cross-reactivity. To train a model that can generalize across this landscape, you need a lot of examples. Right now, we just don’t have enough.¹²

For instance, while there are thousands of known antibody structures, only a few hundred TCR structures are available (just over 600, according to the STCRDab database). Even at the sequence level, most datasets focus on one chain at a time, and there’s a lack of high-throughput assays that can comprehensively sample epitope-paratope pairs. That leaves big gaps in coverage.

One workaround is to pre-train models on broader protein-protein interaction datasets—where more data exists—and then fine-tune them for epitope-paratope predictions. It helps, but it’s not a perfect fix.

2. Bias and Noise in Existing Datasets

Even when data is available, it often comes with issues. A big one is sampling bias.

Because researchers tend to focus on antigens of biomedical interest, these targets are over-represented. That makes it hard to confidently label “negative” examples—cases where no immune response occurs. This results in models that are skewed toward well-studied antigens and perform inconsistently across different epitopes or HLA types.

Add in variability from different experimental methods, and things get even messier. For example, mass spectrometry, a key tool for identifying peptide-MHC complexes, has known blind spots—certain amino acids like cysteine are harder to detect, which introduces bias at the molecular level.

If left unaddressed, these issues can snowball. Biased data leads to biased models, which can lead researchers to choose future targets based on already-skewed predictions. It’s a feedback loop that risks reinforcing blind spots in our knowledge.¹³

Toward Better Data and Smarter Models

So, what’s the path forward?

First, the field needs a community-wide effort to generate, curate, and share high-quality, internally consistent datasets. ML researchers can’t work in a vacuum—they need better access to well-labeled, diverse immune data that reflects the real complexity of biological systems.

Second, ML methods themselves need to be more flexible and bias-aware. That could mean:

• Designing models that can be easily retrained as new data comes in,
• Accounting for known biases during model development (like correcting for mass spec detection issues),
• Using approaches trained on positive-only or positive vs. unlabeled data to get around the lack of confirmed negatives,
• And applying techniques like Bayesian inference to explicitly model uncertainty in database labels.

None of these solutions alone will fix the problem—but together, they offer a path toward more robust, generalizable ML models that are truly useful for immunology.¹⁴

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Want to Learn More?

Machine learning is already shaping how we study immunity and design vaccines—but it’s still early days. The potential is huge, especially as datasets grow and models become more interpretable and adaptive. To get there, the field needs continued collaboration between immunologists and ML researchers, better data, and thoughtful strategies to handle the complexity and noise that come with biological systems.

It’s not just about building smarter models—it’s about building models we can trust, understand, and use to drive real progress in human health.

If this article has sparked an interest, why not check out some of the below topics:

• Machine Learning (ML) Algorithms | A Guide
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References and Further Reading

1. World Health Organization. (2021). Vaccine efficacy, Effectiveness and Protection. World Health Organisation. Available at: https://www.who.int/news-room/feature-stories/detail/vaccine-efficacy-effectiveness-and-protection (Accessed on 29th March 2025)
2. ‌He, X., Su, J., Ma, Y., Zhang, W., & Tang, S. (2022). A comprehensive analysis of the efficacy and effectiveness of COVID-19 vaccines. Frontiers in Immunology, 13. DOI:10.3389/fimmu.2022.945930
3. ‌Tregoning, J. S., Flight, K. E., Higham, S. L., Wang, Z., & Pierce, B. F. (2021). Progress of the COVID-19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape. Nature Reviews Immunology, 21(10), 1–11. https://doi.org/10.1038/s41577-021-00592-1
4. ‌Orenstein et al., (2025). Field evaluation of vaccine efficacy. Bulletin of the World Health Organization, 63(6), 1055-68. PMID: 3879673; PMCID: PMC2536484.
5. ‌Garg, A., & Mago, V. (2021). Role of machine learning in medical research: A survey. Computer Science Review, 40, 100370. DOI:10.1016/j.cosrev.2021.100370
6. ‌Lee et al., (2016). Machine Learning for Predicting Vaccine Immunogenicity. Interfaces, 46(5), 368–390. DOI:10.1287/inte.2016.0862
7. ‌Bravi, B. (2024). Development and use of machine learning algorithms in vaccine target selection. Npj Vaccines, 9(1). DOI:10.1038/s41541-023-00795-8
8. ‌Wistuba-Hamprecht et al., (2024). Machine learning prediction of malaria vaccine efficacy based on antibody profiles. PLoS Computational Biology, 20(6), e1012131–e1012131. DOI:10.1371/journal.pcbi.1012131
9. ‌KABAGENYI, J. (2020). APPLICATION OF MACHINE LEARNING IN IDENTIFYING ASSOCIATIONS BETWEEN CONGENITAL INFECTIONS AND IMMUNE DEVELOPMENT IN INFANTS. Pu.ac.ke.
10. Chaudhury et al., (2019). Combining immunoprofiling with machine learning to assess the effects of adjuvant formulation on human vaccine-induced immunity. Human Vaccines & Immunotherapeutics, 16(2), 400–411. DOI:10.1080/21645515.2019.1654807
11. Benkeser, D., Gilbert, P. B., & Carone, M. (2019). Estimating and Testing Vaccine Sieve Effects Using Machine Learning. Journal of the American Statistical Association, 114(527), 1038–1049. DOI:10.1080/01621459.2018.1529594
12. ‌Bravi, B. (2024). Development and use of machine learning algorithms in vaccine target selection. Npj Vaccines, 9(1). DOI:10.1038/s41541-023-00795-8
13. ‌Tilala et al., (2024). Ethical considerations in the use of artificial intelligence and machine learning in health care: A comprehensive review. Cureus, 16(6), e62443. DOI:10.7759/cureus.62443
14. Farahani, A. F., & Kasraei, N. (2024). Evaluating the Impact of Artificial Intelligence on Vaccine Development: Lessons Learned from the COVID-19 Pandemic. medRxiv (Cold Spring Harbor Laboratory). DOI:10.1101/2024.10.23.24315991

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