Can AI Help Stop the Next Pandemic Before It Starts?

Can AI Design the Next Vaccine? Inside the Race to Outsmart Future Pandemics

A future outbreak can begin with something tiny: a genetic sequence from a virus scientists have never seen before. The next question is enormous: which part of that virus should a vaccine target? Artificial intelligence can help researchers compare millions of biological patterns, predict promising antigens, model protein structures and narrow a huge search space to a much shorter list of candidates.

That does not mean an AI can press a button and “invent a vaccine.” Vaccines are biological products given to healthy people, so the safety bar is exceptionally high. Every promising computer prediction still has to survive laboratory experiments, animal studies, manufacturing checks and clinical trials in humans. AI can make the search faster and smarter; it cannot replace the evidence that proves a vaccine works.

In 2026, results from an important Phase I study made that distinction especially clear. The pEVAC-PS candidate—a pan-sarbecovirus DNA vaccine built around a computationally designed antigen—had been tested in 39 healthy adults between 2021 and 2023. The trial was primarily about safety, not proof of protection. It found no significant safety concerns, while immune responses were measurable but modest in the setting of substantial pre-existing coronavirus immunity. The milestone was not that AI had “solved” coronavirus vaccination. It was that a broadly targeted, computer-designed antigen had made it into human testing and produced biologically meaningful signals worth studying further.

AI can screen enormous numbers of viral structures and antigen candidates, but scientists still decide which ideas are worth taking into the laboratory.

First, What Does It Mean to “Design” a Vaccine?

A vaccine does not need to show the immune system an entire dangerous pathogen. Often, it only needs to present a recognizable piece of it—or instructions that make our cells briefly produce that piece. The immune system then learns what to look for and builds memory cells that can respond faster if the real infection appears later.

The recognizable target is called an antigen. Think of it as the face on a wanted poster: the immune system does not need every detail of a pathogen, but it does need a distinctive enough pattern to recognize the threat quickly. An epitope is an even smaller patch on that antigen—the specific feature that an antibody or immune cell may bind to.

The difficult part is choosing the right target. A virus may contain thousands of possible molecular features. Some are hidden from the immune system. Some mutate rapidly. Some trigger weak immunity. Some look promising in a computer model but fail in living tissue. Vaccine development is therefore a search problem as much as a manufacturing problem—and search problems are exactly where modern machine learning can be useful.

A pathogen’s genome can contain thousands of possible clues. AI helps researchers narrow them down to a much smaller set of vaccine targets worth testing experimentally.

Where AI Enters the Vaccine Pipeline

The phrase “AI vaccine” is convenient but imprecise. There is no single AI system that develops a vaccine from start to finish. Different models help with different jobs: comparing genomes, predicting protein structure, estimating which peptides may be recognized by immune cells, ranking candidate designs, analyzing trial data and scanning safety reports for unusual patterns.

1. Reading the pathogen faster

When a new virus is sequenced, scientists suddenly have a long string of genetic information. AI can compare that sequence with databases of known viruses and proteins, identify conserved regions and flag parts that are changing rapidly. That matters because the best vaccine target is often not simply the most obvious protein—it may be a region that the pathogen cannot easily mutate without damaging itself.

This is one reason genomic surveillance and AI fit naturally together. Surveillance tells us what the pathogen is doing in the real world. Machine learning can help turn millions of sequences into patterns that human teams can investigate.

2. Predicting epitopes—the pieces the immune system may notice

Human immune systems are not identical. Molecules called HLA proteins differ between people and populations, and they help determine which fragments of a pathogen are presented to T cells. A vaccine that produces a strong response in one genetic background may not perform exactly the same way in another.

Machine-learning models can estimate which viral fragments are likely to bind to particular HLA molecules and which epitopes may be immunogenic. This can help researchers prioritize candidates that are more likely to work across genetically diverse populations. It does not prove that an epitope will work in a vaccine, but it can reduce the number of weak candidates entering expensive laboratory testing.

3. Predicting protein structure

Proteins are not flat strings of amino acids. They fold into three-dimensional shapes, and antibodies often recognize shape. That means the same protein sequence can be useful or useless as a vaccine antigen depending on whether researchers preserve the right structure.

Modern structure-prediction and protein-design tools can help scientists model how an antigen folds, which surfaces are exposed and how mutations may alter antibody binding. AlphaFold helped transform structural biology, but structure prediction is only one piece of vaccine design: an antigen can have the “right” shape and still fail to produce useful immunity.

4. Designing antigens instead of simply copying nature

The most ambitious approach goes beyond finding a natural viral protein. Researchers can use computational design to build an antigen that combines useful features from many related viruses or stabilizes a vulnerable molecular shape so the immune system can see it clearly.

At this point, computation becomes more than a search tool. Researchers are no longer limited to asking which natural protein to copy. They can also ask what kind of protein could be engineered to expose the most useful conserved features to the immune system.

The key distinction AI can propose or rank vaccine designs. Biology still decides whether those designs work. A prediction is not immunity, and an immune response is not automatically proof of protection.

A Real-World Precedent: Computational Design Reaches an Authorized Vaccine

A major precedent arrived before today’s generative-AI boom. SKYCovione, a COVID-19 protein vaccine developed by SK bioscience with researchers from the University of Washington Institute for Protein Design, used a computationally designed protein nanoparticle and was authorized in South Korea in 2022. CEPI describes it as the first authorized vaccine developed with AI-enabled computational design methods.

That did not make AI-designed vaccines routine. It showed something narrower but important: computer-engineered biological structures can survive the real path from design and manufacturing to clinical trials and regulatory review.

A Broad Coronavirus Vaccine Design Reaches Human Testing

The pEVAC-PS study makes the idea more concrete. Researchers from the University of Cambridge and DIOSynVax used their Digital Immune Optimised Synthetic Vaccine platform to design an antigen around features shared across sarbecoviruses—the coronavirus branch that includes SARS-CoV-2, the original SARS virus and related animal viruses with spillover potential. The aim was not to match one circulating strain perfectly, but to teach the immune system to notice features that are harder for the viral family to change.

The antigen was delivered as a DNA vaccine using a needle-free intradermal jet injector. In the Phase I dose-escalation study, 39 healthy adults aged 18 to 50 received the candidate. The primary question was safety and tolerability; researchers also measured immune responses.

The vaccine was well tolerated at all tested doses, with no significant safety concerns reported in the trial. Immunogenicity was harder to interpret because participants already had substantial and uneven coronavirus immunity from previous vaccination and Omicron-era infections. Overall responses were modest, but researchers still detected measurable responses to conserved vaccine-encoded sarbecovirus epitopes. That supports the feasibility of the design strategy; it does not yet show that the vaccine prevents infection or disease.

That is the result worth remembering: not “AI created a universal coronavirus vaccine,” but “a computationally designed broad antigen was safe enough to complete a first-in-human study and generated signals that justify further testing.” Phase II studies are needed to learn more about the breadth and durability of those responses.

Designing a vaccine is only the beginning. AI can also help monitor manufacturing, detect anomalies and keep millions of doses within strict quality limits.

Why Universal Vaccines Are Such a Big Deal

Traditional outbreak response is usually reactive. A pathogen emerges, scientists characterize it, a vaccine is designed, manufacturing begins, and clinical studies start. That sequence can be extraordinarily fast by historical standards—the COVID-19 vaccines proved that—but the clock begins after the threat is already visible.

A broad-spectrum vaccine tries to move part of that work earlier. Instead of targeting only one strain, it targets conserved features shared across a group of related viruses. If a new member of that family spills into humans, such a vaccine might provide some protection—or at least give developers a much better starting point. “Universal” therefore does not mean a vaccine against every virus; it usually means broader coverage within a defined viral family.

AI is useful here because “conserved across a viral family” is a data-intensive question. Researchers may need to compare huge numbers of sequences, structures, mutations and immune-response datasets. A machine can scan that landscape faster than a human team, but scientists still have to decide which patterns are biologically meaningful.

Why mRNA and AI Fit Together So Well

AI and mRNA solve different parts of the problem. AI can help decide what antigen to encode. An mRNA platform can make it relatively fast to manufacture a candidate once the genetic instructions are chosen. This modularity is one reason mRNA is important for pandemic preparedness.

But a platform that is fast to redesign is not automatically safe or effective. Change the antigen and you may change the immune response. Change the formulation and you may change how the product behaves in the body. Even with a familiar platform, regulators still need evidence for the specific vaccine being manufactured and tested.

In other words: AI can shorten the design loop, and mRNA can shorten the production loop. Neither removes the clinical evidence loop.

Can AI Predict Which Vaccine Will Work Best?

Partly—but this is still one of the least mature parts of the field. Machine-learning systems can combine pathogen genomics, antigen structure, immune assays and previous vaccine studies to rank candidates. Researchers are also exploring “immune digital twins”: computational models that try to estimate how different immune systems may respond to a vaccine. For now, these are research tools, not substitutes for human trials.

The difficulty is that immunity is an extraordinarily complex biological network. Antibody level is only one part of protection. T-cell responses, mucosal immunity, immune memory, age, previous infections, genetics and underlying disease can all matter. A model can learn only from the data it has seen, and vaccine datasets are often much smaller and less standardized than the internet-scale datasets used to train general AI systems.

A 2026 systematic review reached a similar conclusion: AI and machine learning are increasingly useful across antigen discovery, epitope prediction and vaccine design, but translation is still limited by uneven data quality, restricted external validation and gaps between model performance and proven protection in living systems.

AI Can Also Help After the Vaccine Is Designed

Clinical trial design

AI can help identify trial sites, estimate recruitment needs, analyze complex immune-response data and look for subgroups that respond differently. During an outbreak, models may also help planners choose locations where a trial can answer its question quickly—without changing the scientific standards the trial has to meet.

Safety monitoring

Once thousands or millions of people receive a vaccine, safety systems collect enormous numbers of reports and health records. Machine learning can help detect unusual patterns that deserve investigation. It cannot decide by itself whether a vaccine caused an event, because correlation is not causation, but it can help human pharmacovigilance teams find signals earlier.

Manufacturing and quality control

Biological manufacturing is sensitive to small changes. AI systems can monitor production variables, detect deviations and help optimize processes. This matters during a pandemic because a perfect laboratory vaccine is useless if it cannot be produced reliably at scale.

Regulatory preparation

A less visible bottleneck is the regulatory evidence package. Agencies need structured data from preclinical studies, manufacturing and clinical trials. AI can help organize, cross-check and summarize large submissions, potentially saving time. But the regulatory decision itself is a judgement about uncertainty, benefit, risk and public health—not a document-processing task.

A promising AI-designed antigen still has to face the same reality as every other vaccine: real people, real immune systems and carefully monitored clinical trials.

The 100 Days Mission: Could the Next Vaccine Arrive Before a Pandemic Explodes?

The Coalition for Epidemic Preparedness Innovations, or CEPI, has set an ambitious goal known as the 100 Days Mission: the world should be able to develop vaccines against a newly identified viral threat with pandemic potential within roughly 100 days. It is not a promise that every outbreak can be stopped on that timetable. It is a preparedness target meant to push surveillance, vaccine platforms, manufacturing, clinical networks and regulation to become ready before the emergency begins.

AI is becoming part of that architecture. CEPI is developing a Pandemic Preparedness Engine—an AI-enabled system intended to connect genomic surveillance, epidemiology, vaccine design, preclinical data, clinical trial knowledge, regulatory information and manufacturing. The idea is less “ChatGPT invents a vaccine” and more “a scientific copilot helps global teams find the right evidence and decisions faster.”

In a future outbreak, such a system could take in a new viral sequence, compare it with related pathogens, identify conserved targets, propose antigen designs, suggest suitable vaccine platforms and help teams prepare manufacturing and regulatory work in parallel. It could also connect those decisions with epidemiological data as the outbreak develops.

That ability to work in parallel may matter as much as the AI itself. During COVID-19, speed came largely from overlapping steps that would normally happen one after another: manufacturing at financial risk, rapidly enrolling large trials and sharing data globally. AI could make that parallel response better informed, but it cannot remove the need for any of those steps.

The long-term goal is not an AI that predicts every pandemic. It is a global system that can identify a new threat, understand it and begin developing countermeasures dramatically faster.

The Hard Limits AI Cannot Code Away

Biology can surprise us

A model can predict that an antigen looks promising and still be wrong. The immune system may focus on a different region. The antigen may be unstable. Protection may fade too quickly. A response that looks good in mice may not translate to humans. Vaccinology has many examples of scientifically reasonable ideas that failed in trials.

Better prediction needs better data

AI inherits the weaknesses of its training data. If most immune-response data come from a narrow set of populations, the model may perform less well elsewhere. Global vaccine design therefore needs globally representative genomics and immunology—not only bigger models.

Rare safety problems are statistically difficult

A Phase I trial with dozens of volunteers can detect common short-term problems, but it cannot rule out rare adverse events. That is why vaccine safety evidence grows across multiple trial phases and continues after authorization through pharmacovigilance systems.

Viruses evolve

Even a “universal” vaccine is universal only within a defined biological scope. A pan-sarbecovirus vaccine is not a vaccine against all viruses. Evolution can also produce combinations of mutations that are difficult to anticipate. Broad vaccines may reduce the need to chase every variant, but no serious scientist can guarantee that one design will cover every future threat.

AI creates biosecurity questions

The same biological design tools that can help engineer better vaccines can also increase the power of biological modelling. That creates genuine dual-use concerns. CEPI is therefore building access controls, secure computing, researcher vetting and real-time biosecurity monitoring into its Pandemic Preparedness Engine. In this field, faster science has to come with stronger safeguards.

Will AI Prevent the Next Pandemic?

Probably not—and that is the wrong standard.

Pandemics are not caused only by slow vaccine design. They are shaped by surveillance gaps, delayed political decisions, weak health systems, manufacturing capacity, public trust, supply chains, animal-to-human spillover and international coordination. An AI model cannot manufacture a billion doses, run a hospital, convince a hesitant population or deliver vaccines to a remote clinic.

Where AI may matter most is in reducing avoidable delay: searching for useful biological targets, connecting evidence scattered across databases and helping teams decide what deserves an experiment first. If that saves weeks at the beginning of an outbreak, those weeks can matter enormously.

What the Next Decade Could Look Like

The most realistic future is a connected scientific system rather than a fully autonomous “vaccine AI.” Global surveillance detects a suspicious pathogen. Computational models compare it with known viral families and propose broad antigen designs. Platform technologies such as mRNA or recombinant proteins turn those designs into physical candidates. Automated laboratories test many versions in parallel. Human scientists review the evidence, regulators coordinate early, and manufacturing networks prepare while clinical data are still being generated.

Over time, vaccine design and pandemic preparedness may start to merge. Researchers could build prototype vaccines for high-risk viral families before an outbreak, maintain libraries of pretested platform components and update them rapidly when a specific threat appears. The goal is simple: do not start from zero when the clock is already running.

That is more plausible than trying to predict the exact virus that will cause the next pandemic. We may never know which pathogen will spill over next. But we can prepare for families of threats, and pattern-finding across large biological datasets is exactly where AI can be useful.

Conclusion: Faster Preparedness, Not an Autonomous Vaccine AI

The clearest value of AI in vaccine development is upstream: narrowing the search space before expensive biology begins. It can help teams identify conserved targets, prioritize candidate designs and connect evidence that would otherwise take much longer to assemble.

Then the process becomes stubbornly physical. Cells have to express the antigen. Immune systems have to respond. Manufacturing has to be reproducible. Volunteers have to be followed. Rare safety problems have to be watched for after wider use. A model can rank candidates; it cannot certify immunity.

The first human data from computationally designed broad vaccine antigens show that this approach is moving beyond simulation. They also show why caution matters: a safe Phase I result with measurable immune responses is encouraging, but it is still an early step on the road to proven protection.

If AI helps scientists reach better candidates weeks earlier, that alone could be transformative during an outbreak. The real achievement may not be a headline about a vaccine “invented by AI.” It may be a future emergency in which scientists already know where to look, what to test and how to move faster without lowering the standard of evidence.

That would be a quieter kind of breakthrough—but potentially a far more useful one.

FAQ

Can artificial intelligence create a vaccine by itself?

No. AI can help identify targets, predict epitopes, model protein structures and propose antigen designs, but laboratory testing, manufacturing validation and human clinical trials are still essential.

Has an AI-designed vaccine already been tested in humans?

Yes—but the timing needs a little precision. The pEVAC-PS pan-sarbecovirus vaccine was tested in 39 healthy adults between 2021 and 2023, and the Phase I results were published in 2026. The study found no significant safety concerns and detected measurable immune responses, but it did not prove that the vaccine prevents infection or disease.

Can AI make a universal vaccine?

AI can help design broad antigens that target conserved features shared by a family of viruses. In this context, “universal” usually means broad coverage within a defined viral group—not protection against every virus.

Will AI eliminate the need for vaccine clinical trials?

No. Computer models cannot fully predict human immunity or rare adverse events. Clinical trials remain necessary to establish safety, immune response and effectiveness.

Could AI stop the next pandemic?

AI may help shorten parts of vaccine development and improve preparedness, but pandemics also depend on surveillance, manufacturing, public health, logistics, trust and international coordination. AI is one tool in a much larger response system.