AI for Science Strategy is sponsored by UK Government. Allocations to support AI-driven research in priority areas.

AI for Science Strategy - GOV.UK This publication is available at https://www. gov.uk/government/publications/ai-for-science-strategy/ai-for-science-strategy Foreword by Minister for AI and Online Safety and Minister for Science, Innovation, Research and Nuclear Nothing reflects the UK’s capacity for innovation and creativity more strongly than our centuries-long history of transformative scientific discovery.

The laws of motion, natural selection, vaccination, antibiotics, the structure of DNA – we have made discoveries that changed the world, making us healthier, safer, happier, and more prosperous. The UK’s role in advancing artificial intelligence ( AI ) has continued this heritage.

Turing, Babbage and Lovelace provided the foundations on which modern AI stands, and modern pioneers like Demis Hassabis and John Jumper have demonstrated its capacity to accelerate scientific discovery in areas like protein folding.

These breakthroughs aren’t just about individuals, they’re also about institutions – the universities, research institutes, academies and private sector companies that have nurtured scientific excellence for generations and will continue to do so in this new era. The worlds of artificial intelligence and scientific research are already converging, with enormous implications for the future of science.

For the UK, that presents both a huge opportunity and a profound risk. If we move fast, we have a chance to supercharge our scientific productivity and establish UK leadership during a period of unprecedented scientific innovation. Too slow, and without a unified approached, and our scientific institutions could fall behind – leapfrogged by more ambitious and agile emerging leaders.

Beyond research productivity, the potential for new companies and rapid growth in areas from pharmaceuticals to material science is both real and imminent.

With this in mind, our AI for science vision is centred around: developing a data landscape that facilitates transformative research; ensuring the right researchers have access to compute resource at sufficient scale; building research communities made up of truly interdisciplinary teams; and ensuring we capitalise on rapid developments in autonomous laboratory infrastructure and general-purpose and specialist AI science tools.

Across these priorities, the strategy sets out actions to ensure the UK’s scientific ecosystem not only adapts to, but benefits from the AI for science revolution. The strategy also launches AI for science missions - bold and ambitious targets which capitalise on UK academic and industry strengths and aim to supercharge scientific progress enabled by AI .

The strategy kicks off by launching the first mission, focused on harnessing the technology to speed up the research of new drugs and treatments. In January, we published the AI Opportunities Action Plan – which detailed the macro-scale foundations needed to cement UK leadership in AI and unlock economic growth. To deliver on that plan, government is investing £2 billion between 2026-2030.

Our AI for Science Strategy – which will direct up to £137 million of that investment – provides a powerful complement to the action plan, delivering on its ambitions in the context of AI and scientific discovery. The strategy complements wider interventions towards achieving our AI for Science ambitions such as our investments in compute and the Sovereign AI unit.

It is also closely connected with our UK Modern Industrial Strategy , targeting 5 priority areas representing frontier industries and technologies across the eight industrial strategy sectors – advanced materials, nuclear fusion, medical research, engineering biology, and quantum technology.

This strategy stands as a beacon for the UK’s ambitions in AI for science, signalling both the scale of our commitments and setting clear direction for the rest of the ecosystem. In taking forward the actions set out in this plan, we won’t just be keeping pace with developments in AI for science – we’ll be defining its future.

Minister for AI and Online Safety in the Department for Science, Innovation and Technology Minister for Science, Innovation, Research and Nuclear in the Department for Science, Innovation and Technology ( DSIT ) and the Department for Energy Security and Net Zero ( DESNZ ) Introduction: the AI for science opportunity An ‘ AI for Science’ moment is unfolding.

Investment and attention are being drawn to the idea that increasing the productivity of scientific research will be the most valuable application of AI . [footnote 1] Science is being transformed at unprecedented pace. In the last 3 years, the leading edge of AI for science has moved from prediction to action.

AI models are increasingly autonomous participants in the scientific process. In biology, AI models have gone from predicting the structures of known proteins to designing entirely new ones, helping scientists to accelerate research in nearly every field – from fighting malaria to potential new Parkinson’s treatments.

[footnote 2] Alongside increasingly capable narrow models, frontier AI labs are now racing to build AI science agents capable of automating core parts of the scientific process. These systems can generate hypotheses, design experiments and conduct analysis without direct human input.

Pairings of AI with real-world experiments foreshadow systems that can ‘learn from doing’ in real time, as demonstrated by Liverpool’s Materials Innovation Factory, which built a mobile robotic chemist that conducted 688 experiments over eight days and discovered a new catalyst without human intervention. [footnote 3] These AI -centred discovery processes could transform scientific productivity and progress.

[footnote 4] AI in science matters because supercharged science productivity is a societal shift that dramatically improves lives. AI could slash the average drug discovery timeline; improved weather forecasts and flood predictions could safeguard national infrastructure and improve food security; and AI -augmented plasma control in fusion reactors could hasten progress to limitless clean energy.

The UK ranks 4th in world for the quality of its AI research [footnote 5] and 3rd in the world as a destination for elite AI researchers to work. [footnote 6] Homegrown startups like Latent Labs, CuspAI, DaltonTx, and Orbital have emerged from the UK’s vibrant AI for science ecosystem and could be a profound source of UK influence and economic growth.

Isomorphic Labs was launched from DeepMind in 2021, and since then they have paved the way to accelerated scientific discovery for drug design. Strengthening this AI for science ecosystem is central to our strategy, and the Sovereign AI Unit will prioritise these areas for interventions that support AI companies scaling and driving growth in the UK. Our universities and research institutions are among the strongest in the world.

Across the UK they are moving to capture the AI for science opportunity to improve lives. Moorfields eye hospital and University College London’s RETFound model can not only detect sight-threatening eye illness, but also predict heart disease. [footnote 7] This example, like many others, is underpinned by developments underway at our superb research institutions, data holders, and national facilities.

Organisations like the Francis Crick Institute, the Sanger Institute, the Henry Royce Institute, the Harwell Science and Innovation Campus, UK Biobank, and EMBL ’s European Bioinformatics Institute ( EMBL-EBI ) are all taking steps to transform UK research using AI . The UK is a scientific nation. Scientific discovery is one of the core drivers of human progress.

The UK must act decisively to maintain its scientific leadership and seize the opportunity to shape the transformation of science by AI . We have all the strengths and assets to do so, but we must follow the US, the EU, and others in setting an ambitious national strategy and vision that catalyses the quick action the moment requires. This strategy has 2 objectives.

To develop frontier capability in AI -driven science . The companies and researchers developing general-purpose AI science tools and building autonomous lab infrastructure are transforming the process of discovery. It is a crucial strategic area to build UK capacity.

To ensure the UK retains its position of global scientific leadership . The integration of AI into science is going to reshape the national and global research landscape. We must adapt to this transformation of science to create growth and capture the benefits for public good.

The first objective will be addressed directly in the opening section on AI -driven science . The second objective will be addressed by actions across 3 pillars: The adoption of AI in science, as everywhere, will be fast in some fields and slower in others. Whilst the UK is genuinely world leading in many areas, we must accept that cannot be true in every domain.

As such, the actions in this strategy will be targeted towards five broad priority areas identified on the basis of existing UK strength, alignment with wider UK strategy - including the Modern Industrial Strategy - and opportunities for AI -driven progress.

They are: Targeted interventions within these domains will prioritise near-term impact, creating exemplar cases to be emulated elsewhere in the ecosystem and delivering tangible impacts that are felt by citizens in the near term. Crucially, each of our priority areas are characterised by diverse methodological approaches, research communities, data landscapes, and levels of AI -integration.

In practice, therefore, the delivery of interventions across will require a tailored approach that is sensitive to the needs of specific disciplines. The strategy also launches AI for science missions. These are specific, timebound, ambitious targets that can only be reached through breakthrough scientific progress enabled by AI .

Delivery of this programme of interventions will bring together government’s critical levers – compute resource, data assets, convening power, investment and more – as well as up to £137 million of targeted investment to accelerate AI -driven scientific breakthroughs in areas of UK priority. Government is keenly aware of the importance of responsible AI adoption in science and beyond.

Of particular importance is understanding how scientists will adapt to using new tools that can hallucinate or make errors, and ensuring the adoption of AI does not negatively impact research integrity. Our approach in general will be guided by the national framework on research integrity – which ensures that all UK research is underpinned by the clear set of principles, driving high quality in all research activity.

[footnote 8] Specific actions will be developed with responsible AI at their core. Managing the environmental impacts of research-related activity and reducing the carbon footprint of the UK AI infrastructure and data storage will be considered throughout this strategy.

There are innovation opportunities in sustainable AI for science, and we are looking towards potential applications that increase the productivity and data richness of individual experiments and reduce dependencies on chemical and material usage to deliver an overall reduction in negative impacts across the R&D pipeline.

AI for Science is driving the UK’s modern industrial strategy The Digital and Technologies sector plan names AI as one of 6 frontier technologies which are essential to driving growth, and this strategy targets five priority areas representing frontier industries and technologies across the eight industrial strategy sectors – advanced materials, fusion energy, medical research, engineering biology, and quantum technology.

Across government, there is recognition of the scope for AI -driven progress across these domains. In advanced materials , this includes programmes like The National Materials Innovation Programme, with an initial £50 million committed in the UK’s Modern Industrial Strategy that includes funding to deliver data-driven tools that enhance materials discovery, design, and validation.

AI is transforming engineering biology by enabling novel design, high throughput analysis, and reducing experimental cost. AI advancements can impact across the design, build, test, learn cycle and are accelerating the development of solutions to some of society’s most urgent challenges.

For instance, by unlocking new avenues for drugs that can treat diseases and sustainable biology-derived alternatives to everyday consumer items from chemicals and materials to low-carbon fuels. In medical research , the Life Science Sector Plan makes clear that AI is revolutionising the life sciences sector across research, diagnostics, treatment, and manufacturing.

Significant investment support from across UK Research and Innovation ( UKRI ) and National Institute for Health and Care Research ( NIHR ), plus the £600 million announced for the development of the Health Data Research Service , will enable world class R&D in medical research. Exciting things are also happening in fusion energy and quantum technologies .

AI will be instrumental in the UK’s pursuit of commercialised fusion energy, and Culham has been announced as the UK’s first AI Growth Zone . From data utilisation to materials discovery and plant operations, AI will accelerate the work of the UK’s leading scientists in making fusion energy a reality.

In quantum technology, there is evidence AI can accelerate quantum system design and will in turn unlock simulation capabilities essential for future clean-energy breakthroughs. Current AI models raise the possibility of autonomous scientific reasoning. Already, AI science agents can generate novel, testable hypotheses and design experiments when given a research problem.

In many disciplines the outputs must cross into the real world, and where physical experiments are needed, there is rapidly growing interest in coupling machine learning systems to robotic synthesis and characterisation in autonomous labs. Investors and institutions drawn to this idea are moving fast.

In the UK, researchers at the Whittle lab in Cambridge and Liverpool’s Materials Innovation Factory are building leading AI -driven science capabilities. Companies like Lila Sciences, Future House/Edison Scientific and Periodic Labs have collectively raised almost $1 billion of funding with the vision of automating the process of scientific discovery.

In some areas, achieving this extraordinary ambition seems an entirely plausible near-term possibility. The hardest steps require breakthroughs moving beyond simulated or narrowly controlled domains, real-world validation of the efficacy of AI -designed therapeutics and materials, and reproducible evidence that AI can accelerate discovery in a way that tangibly benefits scientists and citizens.

The prospect of AI automating scientific discovery is disruptive, even in limited instances, and profoundly challenges much current research practice. It is also several steps away. We should confront the implications of continued progress with a clear understanding of the capabilities and limitations of emerging AI science systems – and position the UK as a beneficiary of the change to come.

Actions ( AI -driven Science) Action 1: Accelerate the development of AI -driven science in the UK Why it matters : AI -driven scientific progress could fundamentally alter the nature of discovery. Interfacing AI with the real world is a barrier to validation and progress in many areas and, beyond that, there is strong evidence autonomous labs will substantially accelerate scientific research in their own right.

Delivery : we will support research teams already taking rapid action to lead the way on autonomous labs and the development of AI -driven science in the UK: the Sovereign AI Unit will launch an open call on autonomous labs, seeking proposals to develop or scale autonomous lab platforms in the UK. This call will target the huge potential benefits to both data generation and AI -driven discovery.

‘Closed loop’ systems capable of analysing results in real time and using them to autonomously control further experiments will enable the generation of data that is uniquely high-quality and sensitive to the needs of model developers.

We will work with teams building AI systems that target the full end-to-end workflow of scientific knowledge creation to develop a framework for safe deployment in the UK science ecosystem, including with the Advanced Research and Invention Agency ( ARIA ) through their call for exploratory ‘ AI scientist’ proposals.

This work will also consider domain specific issues of responsible and safe adoption, such as biological security implications. Lastly, government will explore access-models that enable safe, transparent use of general-purpose AI science tools within the UK research ecosystem.

Action 2: Fund research into the methodological implications of integrating AI into scientific research, building on the work of the UK Metascience Unit Why it matters : AI is not just driving new breakthroughs in science, it is fundamentally reshaping the way science is conducted, evaluated, and understood.

Metascientific investigations of AI -enabled science are needed to better understand how scientific processes are changing and to ensure that methodological developments are improving, not undermining, the integrity, novelty, and quality of scientific research.

UKRI and DSIT ’s joint UK Metascience Unit has already taken critical steps in this space to improve our understanding of how the growing adoption of AI is changing the research landscape and how governments, industry, and funding organisations should respond.

The unit has funded 18 early career fellows to look at how AI is changing science, looking at topics including the use of AI in peer review, evidence synthesis, and in the generation of synthetic data, and how these changes impact the productivity, creativity and wellbeing of researchers.

In addition to this work, the Unit will conduct a comprehensive ‘National AI in Research Survey’ to better understand the diffusion and adoption of AI in various aspects of the research progress across fields and career stages. Automating Discovery: The Materials Innovation Factory The Materials Innovation Factory ( MIF ) is an £81 million facility co-founded by the University of Liverpool and Unilever.

The MIF is the most advanced autonomous lab in Europe, using robotics and AI to conduct high-throughput, ‘closed-loop’ campaigns of experimentation to generate materials data at unprecedented scale. In November 2025, Liverpool announced it would build on the success of the MIF by launching an AI Materials Hub for Innovation – creating a flagship national facility in materials science.

Facilities like the MIF provide a window into the future of AI -driven science. Not only are they driving advancements in the integration of robotics and AI , they represent a profound shift in the changing human dimensions of science at the frontier.

Interdisciplinary teams of chemists, roboticists, machine-learning scientists and engineers from the public and private sector work together under one roof, developing AI tools that will change the face of chemistry and materials science, as well as creating new products and ventures that fuel UK growth.

The MIF also shows that the future of AI -driven science is already growing organically across the UK, with a top British university and industry leader collaborating to file over 200 patents, create over £400 million of annual sales growth, [footnote 9] and drive progress at the frontier of materials science. This section views data as a key ingredient in the AI for science recipe.

That means a few things: it means datasets are only constitutive parts of a larger whole it means what’s good today might not be good tomorrow In essence, we need to take an intentional and targeted approach to generating new high-quality datasets and optimising existing data. The actions below assume our objective is not to achieve maximum data generation, but to ask: ‘ what data do we need to solve the challenges we face?

’ On the question of whether we should generate new data or optimise existing data to make it ‘ AI -ready’, we will prioritise the former. Newly generated datasets have the advantage of being precisely tailored to specific research challenges, and advancements in lab automation are increasing the speed at which new data can be generated.

That said, there are notable exceptions when historical data cannot be replicated or is rooted in time: the decades of legacy fusion data held by UK Atomic Energy Authority ( UKAEA ); longitudinal data from the National Survey of Health and Development; and ocean circulation data held by the National Oceanography Centre are all examples.

High-quality datasets are the foundation of AI -enabled scientific breakthroughs and represent a growing category of internationally important strategic assets. The development of scientific datasets in areas of mutual priority is a stated objective of the recently signed UK-US Tech Prosperity Deal , and exemplifies the value of international collaboration under this pillar.

Equally, the targeted identification and hosting of high-value datasets can be a powerful locus for building UK community and stronger national ecosystems. Work here is already underway. In June, the Sovereign AI Unit provided seed funding to the OpenBind consortium to generate foundational protein-ligand structural data to power the next era of AI for drug design.

Independently of the data it generates, OpenBind is a powerful model for the effective consortia we need to build, and we are committed to developing that model to ensure lab automation and data generation are central to the UK’s AI for science leadership. Alongside dataset generation, storage is critical.

The actions in this strategy expand upon efforts to create a streamlined model of federated data access, with data storage in proximity to our national computing centres. These developments will pay close attention to the management of sensitive data to ensure our data ecosystem meets all relevant ethical and legal obligations, as well as appropriate cybersecurity requirements.

Action 3: UKRI will establish a mandate to scale up data storage from experimental runs and simulations at UKRI -owned facilities, labs, and institutes with an aim to ensure that all relevant and useful data from every experiment is stored, curated, and made compliant with Findable, Accessible, Interoperable, and Reusable ( FAIR ) principles by 2030 Why it matters : Alongside the need for a targeted approach to identifying high value datasets to enable scientific breakthroughs, we must also adopt an overarching ‘ AI first’ stance to the production, storage, and optimisation of scientific data.

The UK’s major facilities and research organisations are already working to pioneer an AI -first approach but leadership from government and funders is needed. DSIT and UKRI will support the UK’s leading national laboratories and scientific facilities to generate AI -ready data and pioneer AI -first approaches.

As a first move and signal of intent, the Science and Technology Facilities Council ( STFC ) will uplift the infrastructure capabilities, including those of Harwell’s Diamond Light Source. This will be a critical step towards cementing ‘ AI first’ data generation capabilities at our world class science infrastructure, and UKRI and government will explore options to extend these upgrades across the ecosystem.

More broadly, UKRI will continue modernising its research data policy and working with scientific communities to ensure data generated through UKRI -funded research it funds aligns with FAIR principles, and supports a modernised, AI ready data landscape. This will include exploring the use of AI tools to support researchers in creating and optimising their data.

UKRI is due to publish its revised data policy in 2026, and the forthcoming UKRI AI Strategy will specify details of an AI -first approach, setting out key objectives and concrete actions for normalising and standardising the development of AI -ready data. Action 4: Identify and develop high value datasets that will unlock transformative breakthroughs in scientific priority areas Why does it matter?

: High-quality data is a key ingredient for AI -enabled scientific breakthroughs. The identification and generation of new high value datasets will be critical in unlocking the future transformative potential of AI . DSIT will develop high value datasets to unlock AI for science breakthroughs.

As a starting point, DSIT will collaborate with Renaissance Philanthropy on a call to source and review datasets proposals in the scientific priority areas set out in this Strategy. We are also scaling up an initiative with the Henry Royce institute to curate and centralise high quality AI -ready materials data, building prototype repositories of standardised metadata and APIs.

This will bring us a step closer to an ‘ AI -ready’ approach to physical sciences data, which will advance the use of AI to accelerate the discovery of new materials. Further actions will identify high value datasets through structured discovery processes that take an actively community-led approach, and utilise external engagement, open calls, workshops and hackathons to identify the datasets our scientists need.

A key priority for the programme will be the creation of new data assets for the UK. The data opportunities identified will partially inform the prioritisation of efforts in the overall move towards ‘ AI -first’ data in Action 3. It will also build on our efforts in driving AI -driven science and how can we develop high-value data through automated processes.

Quality Matters: The Protein Data Bank Founded in 1971, the Protein Data Bank ( PDB ) is an open-access repository of protein, nucleic acid, and biomolecular structures. For decades, researchers around the globe submitted hundreds of thousands of experimentally determined structures in highly standardized formats, creating a large and extremely high-quality database.

The PDB is the unsung hero of DeepMind’s AlphaFold breakthrough, and it embodies the tremendous power of well-curated scientific data in the age of AI . The original vision for the PDB was to iteratively develop a single centralised archive of known biological structures. By 2020 researchers had submitted 172,816 experimentally validated entries.

DeepMind used data from the PDB to train AlphaFold, a model which predicts (rather than experimentally validates) protein structures with unprecedented accuracy. In 2021, DeepMind and EMBL-EBI published the predicted structures of 365,000 proteins.

Today, the AlphaFold Database contains predicted structures for over 200,000,000 proteins – marking an extraordinary breakthrough in structural biology and earning Demis Hassabis and John Jumper the 2024 Nobel Prize in chemistry. AlphaFold is a landmark breakthrough in AI for science, but it remains a singular achievement.

We don’t have AlphaFold equivalents in materials science, fluid dynamics or neuroscience because we don’t have PDB equivalents in these disciplines. The PDB was the perfect dataset fifty years in the making. By exploiting new methods of data generation, we will support UK researchers to replicate this feat – ensuring we adapt our approach towards future opportunities that AI -driven science will present.

Action 5: Launch pilot programmes for collecting ‘dark data’, including negative experimental data, to boost model performance in areas of UK priority. Why it matters : The scientific data landscape is characterised by enormous quantities of ‘dark data’, be it unpublished experimental results, details of experimental setups, non-machine-readable data held by large institutions, or negative experimental results.

Mechanisms and incentives for publishing negative data remain limited, and do not reflect the particular value of this data for machine learning. Unlocking the potential of dark data to inform future scientific discovery will be critical in developing precise and robust scientific AI models, removing positive bias.

Working with large data institutions, UKRI will launch 3-5 pilot approaches that use novel methods to capture data that is currently underrepresented in large datasets – for example negative data or details of experimental setups. Pilots will be evaluated in terms of efficiency, scalability, and the usefulness of the data collected. Pilots will take a targeted approach, focusing on enhancing high value datasets identified in action 4 .

DSIT and UKRI will also engage with industry partners to seek agreements around researcher access to privately held negative data, for example in pharmaceuticals or materials.

Action 6: Build large-scale data infrastructure to host high value datasets in proximity to sovereign compute Why it matters : Robust, secure and accessible data storage in proximity to compute is a key resource for training AI models and will only grow in importance over the coming years.

Equally, any compute system must interact seamlessly with data from across multiple, independently managed sites – often with different formats, governance, and access restrictions.

Government will establish a new data repository to be co-located with Isambard- AI at the Bristol Centre for Supercomputing ( BriCS ) and will invest in additional storage capacity at the Edinburgh Parallel Computing Centre ( EPCC ) – the UK’s first National Supercomputing Centre and site of the UK’s next national supercomputing service.

Both data storage facilities will have federated capabilities, ensuring robust compute access via AI Research Resource’s ( AIRR ) ‘AIRRport’ platform. Government will seek to continually evolve the capabilities of this federation to ensure that it provides users with the best tools to conduct transformative research and innovate.

This could include the development of secure agent-to-agent interactions that manage data access within federated systems. Finally, our new AIRR data capability will explore how to provide secure access to high-impact health datasets through the AI Research Resource under strong privacy safeguards, enabling UK scientists to undertake more ambitious AI research using this data.

It will build on the work of the Federated Research Infrastructure by Data Governance Extension ( FRIDGE ) programme [footnote 10] and involve partners like UK Biobank, the world’s most widely used collection of consented and de-identified human biological and health data, to define the governance and technical controls required to deploy trusted research environments ( TREs ) in high performance computing clusters.

Compute is the engine of AI development, and effective utilisation of the UK’s R&D compute infrastructure will be vital to meet the needs of our research community.

Actions in this space take two forms, the: development of hardware, software, workflows and human infrastructure needed to deliver compute access, and creation of targeted allocation pathways by which that compute resource is made available to researchers at sufficient scale The UK is on track to develop a world-class AI compute ecosystem through the £1 billion investment in the expansion of the AI Research Resource ( AIRR ).

Following its formal launch in July 2025, Isambard AI is one of the world’s most powerful supercomputers [footnote 11] and, coupled with the Dawn supercomputer in Cambridge, represents £300 million of investment. In January 2025 the Prime Minister announced the UK’s first AI Growth Zone at the UKAEA ’s campus at Culham in Oxfordshire .

Culham will house powerful AI compute suitable for fusion research and other advanced technological applications. These investments are already bearing fruit, with UK researchers utilising sovereign compute to tackle some of the greatest research challenges of our time. This summer, the Sovereign AI Unit ran an open call for compute access at sufficient scale to support training of state-of-the-art narrow models.

As a direct result, Gábor Csányi’s team at Cambridge are developing their leading materials foundation model, MACE, and researchers at Imperial are developing a foundation model for health in the form of Nightingale AI , which is also supported by Engineering and Physical Sciences Research Council’s ( EPSRC ) GenAI hub.

Wider AIRR allocations are also delivering public benefit, with a research team at the University of Oxford using an award of 10,000 GPU hours to accelerate cancer vaccine innovation – making treatments safer, more precise and more effective. Moving forward, AI for science