ISTA physicists have confirmed a theory proposed 20 years ago that could advance quantum technology. A future quantum computer may need separate modules to behave as one machine, even when they are physically apart. That requires distributed entanglement, a quantum connection between distant components that has typically depended on active control and repeated measurements. Physicists [...]Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to treat conditions across most major acute and chronic diseases), the complexity is even greater.
Scientists explore vast quantities of possible molecules, looking for the rare few that will bind to the right target, remain stable in the human body, and be manufacturable at scale. Today, AI is speeding up these processes and has quickly become a core part of the infrastructure in pharmaceutical R&D.
AI-assisted design is a growing part of how biologic drug candidates are developed, and companies like AstraZeneca are actively building its engineering teams to push this further. “Everything we do, whether it’s design, make, test, or analyze, is now computationally enhanced,” says Puja Sapra, senior vice president and head of R&D biologics engineering and oncology targeted discovery at AstraZeneca. “The cycle times are getting shorter while productivity and innovation increase.”
Sapra explains that AstraZeneca’s approach follows a build-measure-learn loop. AI generates or prioritizes candidate molecules computationally, predicting which designs are most likely to succeed. Scientists then focus lab resources only on the top-ranked candidates. This leads to a tighter feedback cycle with fewer dead ends, faster iteration, and the ability to go after disease targets that were previously considered untreatable by medicine. Because the number of possible molecular combinations far exceeds what any human team can systematically explore, using AI to narrow and refine the options for testing has become a major focus in biologics drug design.
Beyond accelerating timelines, AI is also being applied to the discovery of entirely new classes of medicines. Traditional biologics typically target one disease pathway. The next generation of drugs can hit multiple targets simultaneously or precisely deliver therapeutic payloads to specific cells. Achieving this requires optimization across many variables at once. Looking ahead AI-driven models could help design these increasingly complex, multi-specific biologics, explains Puja Sapra. “For example,” she continues, “such models could help identify which two or three targets to prioritize based on the underlying biology, then optimize across multiple parameters to balance a molecule’s potency, stability, manufacturability, and safety.” “Drugging the undruggable is becoming a reality,” Sapra says. “These technologies will eventually enable us to develop medicines against targets once thought impossible to reach. The potential for benefit to patients is remarkable.”
McKinsey estimates that generative AI, combined with other computational tools, could cut drug discovery timelines by as much as 50%. But every AI model is only as good as its training data. In drug discovery, that means ample quantities of high-quality biological data. Experiments can provide a rich source of such data. Whether they succeed or fail, each experiment generates a signal about what does and does not work.
“Data is our differentiator,” says Sapra, explaining how the company’s datasets are proprietary and multimodal and include molecular structures, binding measurements, safety profiles, and manufacturing outcomes. “We’ve built an intentionally diverse portfolio across multiple disease areas and drug types. All of that data empowers us to fine-tune frontier AI models with richer, more representative training sets.” She continues, “Further, we have invested in deep screening technologies to generate additional datasets required in volume to constantly refine and validate our models.”
To bring all of that data together in one place, AstraZeneca is building what it calls a “lab of the future” facility in Kendall Square, Cambridge, Massachusetts where AI and robotic automation will be able to form a continuous, closed-loop discovery system. “Where a self-driving car uses sensors and models to navigate its environment, this system uses AI to make predictions, robotic systems to execute experiments, and instruments to generate data,” explains Sapra. That data feeds directly back into the models, accelerating each subsequent cycle.
“Throughout, scientists will remain central to the process, providing the oversight, judgement, and strategic direction that ensure outputs are explainable, tolerable, and directed toward potential patient benefit,” she adds.
Eventually, automated high-throughput systems will be able to make and evaluate thousands of molecular interactions on a weekly basis. “This will generate AI-ready data at a scale that traditional workflows cannot match,” Sapra says. “Robotic sample handling, automated quality checks, and integrated data pipelines also have the potential to help accelerate early drug development timelines significantly.”
Ultimately, Sapra says, the end-state vision for AI in biologic drug discovery is what the field calls “de novo” design. For this, the goal is for AI to generate entirely new protein sequences that precisely fit the desired drug properties. This includes designing the structure, predicting safety, how it will behave in the body and how to make it manufacturable.
“The field is making great progress toward a completely AI-generated biologic, designed from scratch all the way to a clinical candidate,” Sapra says. “As we continue to leverage frontier models and fine-tune them with the right datasets, we bring ourselves closer to this reality. I believe it will come. It’s a matter of time.”
Several key elements are needed to reach this point, however. First is richer and more standardized training data across the industry. Second, robust evaluation benchmarks for AI-generated candidates. And third, teams that know how to work at the intersection of machine learning and biology. Of all the prerequisites, however, safety prediction may be the most consequential, and perhaps the least discussed, Sapra says.
“One of the hardest problems in de novo design is predicting whether a computationally generated molecule will be safe in the human body,” Sapra explains. AstraZeneca is tackling this with what amounts to virtual clinical trials. These are advanced cell systems and micro-scale organ models that function as physical testbeds, paired with AI that learns from their outputs.
“These systems have the potential to generate enhanced biological signals without traditional testing bottlenecks, and they’re a critical missing piece in closing the loop between AI-generated designs and clinical-ready candidates,” Sapra adds.
A shift currently underway is the move toward agentic AI systems that can simultaneously generate molecule candidates and predict how efficacious and safe they are likely to be. These autonomous workflows can connect disease-level insights directly to molecule design, bridging what were previously separate data silos. “The complexity of the biology goes hand-in-hand with the design of the molecule,” summarizes Sapra.
The transformation underway in biologics is not just about technology. “With more autonomous systems, human oversight remains at the heart of this approach—ensuring explainable and ethical AI for the benefit of patients,” says Sapra.
For scientists, working with AI is a collaborative process. “Scientists will work hand-in-hand with these model systems,” she says. “There will be a world where models will design molecules, then scientists will work with the systems to test those molecules and put all that data together.” Through this process of human checks, balances, and judgement calls, the models will evolve and constantly improve, ultimately with potential to benefit patients.
For engineers, designing and building effective systems ready for human-AI collaboration will mean ensuring high levels of model transparency and explainability. According to Sapra, AstraZeneca’s engineering teams include data scientists, automation specialists, and AI engineers, who are developing systems that act as “thinking partners” rather than black boxes. “Engineers are designing systems that generate, validate, and learn at speed. And the problems are genuinely hard: Multimodal data fusion, closed-loop optimization, uncertainty quantification, and interpretability at the point of clinical decision-making,” she adds.
In taking on such technically demanding challenges, engineers and scientists have the opportunity to contribute to the research and development of potentially life-changing treatments for many diseases, says Sapra. “The biologic medicines we can develop today, and those we’ll design tomorrow, depend on combining world-class AI and engineering talent with deep scientific expertise.”
This article has been initiated and funded by AstraZeneca. Z4-85058, July 2026.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Company behind ChatGPT says agent ‘cheated’ an evaluation by attacking a Hugging Face database
OpenAI has revealed that an autonomous AI agent powered by its technology went rogue during a test, accessed the open web and hacked a prominent startup by itself in an “unprecedented incident”.
The company behind ChatGPT said the startup Hugging Face had detected and contained the agent – an AI tool designed to carry out tasks without human assistance – which had entered its systems.
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© Photograph: Andre M Chang/ZUMA Press Wire/Shutterstock
© Photograph: Andre M Chang/ZUMA Press Wire/Shutterstock
© Photograph: Andre M Chang/ZUMA Press Wire/Shutterstock
The obituaries were being written before the body was cold. Within hours of Prime Minister Andy Burnham confirming that Whitehall’s responsibilities for science and technology would be absorbed into a new Department for Business, Innovation, Science and Trade (DBIST), Sir Paul Nurse, president of the Royal Society, was on Radio 4 warning that “science isn’t a slave to business”. The trade association TechUK declared it “the wrong change at the wrong time”.
You might imagine that something ancient, ineffable and essential to British science had been ripped away. In fact, the Department for Science, Innovation and Technology (DSIT) was only born in February 2023 – a surprise late tweak to the machinery of government under Rishi Sunak’s faltering premiership.
Science has never enjoyed a settled home in Whitehall. For most of the period from 1995 to 2023, it sat precisely where Burnham has put it now: inside a business super-department. Notably, the single most important protection UK science secured in that era – a ring-fenced budget through the George Osborne austerity years – was won from inside the Business, Innovation and Science department, under then-business secretary Vince Cable.
The historian David Edgerton has long argued that Britain’s fondness for tinkering with the machinery of government is a form of displacement activity. What actually shapes the fortunes of research is money, people and demand for what they produce, not the nameplates on London’s Victoria Street.
On that test, the outlook for science is far calmer than some reactions to this latest move would suggest. Under plans published last October, the UK’s total research and development budget is locked in at £58.5 billion over the four-year spending review to 2030 – a real-terms increase.
Machinery changes move budget lines across departments; they don’t unravel them. As yet, nothing we’ve heard from the Burnham government suggests the settlement for UK Research and Innovation (UKRI) is at risk.
If we want to be reminded what real damage to a science system looks like, we only need to glance across the Atlantic. While London rearranges the departmental furniture and dishes out new email addresses, Washington is taking a wrecking ball to the foundations of its public science system.
A 2027 budget request will halve the size of the US National Science Foundation, if passed. A draft White House rule, due to take effect in October, will hand political appointees the power to vet federal research grants against “the president’s priorities” and terminate awards mid-stream.
A predictable exodus has begun. The Nobel laureate Omar Yaghi has just left the University of California, Berkeley for Tsinghua University in China.
Set against political commissars patrolling the borders of knowledge, the UK’s science soap opera feels deeply provincial – a debate about which secretary of state sponsors oversight of a gradually swelling budget.
And the institutional layer beneath this is in better shape than doomsayers allow. UKRI’s new strategy has set a clear five-year path: priority programmes in AI, quantum and clean energy, faster funding decisions, and a welcome seriousness about experimentation with the modes and means by which we manage public investment in research.
Not everyone is happy with UKRI’s approach to prioritisation. But a funder with a published strategy, agreed budget and hard targets is a more durable asset than a three-year-old ministry. The research councils have outlasted every department that ruled over them, and UKRI will outlast this too.
Nor should the science community be spooked by closer proximity to business. When I leave my office at UCL and stroll down the Euston Road through London’s Knowledge Quarter – dubbed “the new square mile” – I pass by the Wellcome Trust, the Crick and Turing institutes, the British Library, Anthropic and Google DeepMind. Day in, day out, this dense weave of labs, firms and cultural institutions delivers what the triple helix of government, industry and academia is all about.
And we can find versions of the same story unfolding in Manchester, Sheffield, Cambridge and Glasgow. Rather than science being subordinate or “enslaved”, the UK’s world-leading universities have spent the past decade aligning themselves with agendas for regional growth and frontier technologies.
But there’s a harder truth lurking here too. Public money accounts for less than a third of UK research and development investment. The rest is financed and largely performed by the private sector.
UK science’s chronic weakness has never been the quality of publicly funded research. It is the R&D intensity of UK firms, and their ability to convert discoveries into products, services and revenue streams generated and owned in the UK.
If yoking science to industrial strategy in a single department reminds Whitehall to treat these as a single problem, some good may come of Burnham’s reorganisation.
With DSIT’s disappearance, a particular source of concern has been the future of AI policy. This at a time when the radical possibilities and risks of AI frontier models, fluctuating US export controls, and the latest “Sputnik moment” jolt from China’s Kimi K-3 demand sustained and technically fluent policy attention.
We are also seeing a sharp acceleration in the ways AI is transforming science itself – whether by cracking mathematical problems that stumped humans for the past 87 years, or collapsing decades-old models of grant writing and peer review.
Splitting AI from science just as they converge in practice would have been the least defensible element of this reorganisation. So it is reassuring that cross-government oversight of AI, including the AI Security Institute and AI public sector adoption, is moving to a new “AI taskforce” in the Cabinet Office – alongside the promotion of Kanishka Narayan to the status of AI minister attending Cabinet. Much now rests on his shoulders.
Government departments come and go. Those of us who have long advocated for research policies built on evidence rather than gut can allow ourselves a wry smile at the fuss created by this latest reorganisation – and then get back to work.
DSIT is dead, but UK science will be fine. Keep your eyes on the AI prize – and be thankful we are arguing about organograms, not survival.
James Wilsdon receives funding from UKRI and the Wellcome Trust, and a wider consortium of international research funders who partner in the Research on Research Institute (RoRI).
In a world where trust in institutions is mostly in decline, levels of trust in science remain relatively high. Yet at the same time, belief in scientific conspiracy theories, such as hushed-up effects of vaccines or aeroplane “chemtrails”, is also widespread.
What explains this apparent contradiction? Our work suggests we need to ask a different question – not how much people trust science, but which sources inform their understanding of what science is and how it works.
In our recent study of 843 US residents, we found that people who picture scientific progress as the work of lone geniuses, and who learn about science mainly from podcasts and documentaries, are markedly more drawn to conspiracy theories – whatever their level of education.
This fits the bigger picture very well. In a 16-country survey, 70% of respondents believed at least one false or unproven health claim. High trust in science and widespread conspiracy beliefs appear to coexist in the same populations.
We fear that trust in science is compatible with believing almost anything, so long as it is expressed in plausibly scientific terms. This can lead to some people refusing vaccines, trusting miracle cures or dismissing climate warnings – all while remaining convinced that science is on their side.
At school, science is almost always presented as a linear sequence of achievements, or in the form of impersonal theories. The human side of discovery is largely stripped out.
In contrast, most science-focused films and documentaries build their narratives around biographies of exceptional individuals.
These often yield a familiar plot. Society is facing an intractable problem. A genius arrives with the exact solution – but is ignored and mocked until, after much personal pain, everyone realises he (for it is invariably a man) was right all along.
From the martyrdom of Giordano Bruno, the Renaissance friar burned at the stake partly for defending an infinite universe, to A Beautiful Mind, the Oscar-winning portrait of the mathematician John Nash, this lone-genius arc has permeated popular depictions of science for centuries.
Many conspiracy theorists seek to exploit this trope – in part to suggest that those who recognise it must be unusually smart too, living in a world where everyone else is “naive” or “brainwashed”.
Yet these lone genius plots leave out nearly everything that makes science work. Namely, the rather boring and complex processes of accumulation and evaluation, from peer review to replication and endless disagreements, through which reliable knowledge emerges from a crowd of people and institutions.
Research also shows that people presented with academic research “in the making”, without definitive conclusions, often read any hesitations or equivocations as scientific weakness, rather than honesty.
Most people trust science without understanding its technicalities. This means trust can easily be attached to shared cultural markers of “scientificity”: technical vocabulary, charts, credentials, fancy equipment, numbers quoted to impressive precision. In other words, whoever displays the right cues sounds plausibly scientific.
Consider the example of Corrado Malanga, an Italian chemist who developed an elaborate theory of aliens harvesting human souls, and his collaborator Filippo Biondi, a radar researcher. Using satellite radar, the pair claimed in March 2025 to have discovered an elaborate hidden city up to two kilometres beneath the Khafre pyramid in Egypt.
Archaeologists quickly and widely dismissed these claims, in part because this kind of radar cannot see more than a few metres underground. Yet Biondi went on to present their “discovery” at length on The Joe Rogan Experience, one of the world’s most popular podcasts, in January 2026 – attracting many millions of views and listens to this widely disputed “scientific account”.
A recent analysis examined 8.7 million Twitter (now X) posts that shared supposedly fact-checked news stories. It found that references to scientific entities – through use of terms such as scientist, university or study – were associated with an increase in sharing. However, this was especially true of “low-veracity content, and when users lean politically to the right”.
There are two threads intertwining here: a story about how science progresses, and the informal media where it mostly lives.
In our recent study, published in Current Psychology, we measured participants’ “genius mentality” (the propensity to believe that progress comes from exceptional individuals overturning consensus through intuition) and also their science media preference (whether they favoured podcasts and documentaries over expert-vetted sources).
We found people who scored higher on either metric were more likely to endorse conspiracy theories. These correlative effects were stronger than either education or political orientation, factors usually considered major drivers of such beliefs.
We also observed an interesting moderation effect: among people with little or even moderate trust in scientists, a high belief in the genius mentality markedly increased conspiracy belief. Among those who trusted scientists most, it made almost no difference.
From our research, we believe standard remedies, fact-checks and appeals to “trust the science” miss a deeper problem. They may correct claims one at a time, but leave untouched core beliefs (and misunderstandings) about how science works.
A more durable approach is to make the collective machinery visible: to explain why debate, peer review and the slow building of consensus, rather than individual intuition, are what make science reliable.
Some science education researchers argue that the goal should not be to turn everyone into a scientist, but a “competent outsider” – people who can correctly evaluate where knowledge comes from and how it has been produced.
We believe a school science education should not only introduce theories, but explain how the scientific community works on a daily basis. Children need to learn how knowledge emerges from many people cooperating and challenging each other within shared criteria – and that any consensus which survives this process is a strength, not groupthink.
Our ambition is to protect against future conspiracy theories before they emerge, rather than simply trying to correct the last one.
Michele d'Errico receives funding from the Irish Research Council.
Taha Yasseri receives funding from Research Ireland and Workday Inc.
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