Cambridge-based Potential Sciences raises seed funding led by Sovereign AI Fund
This article covers Potential Sciences, a Cambridge-based healthtech startup that has emerged from stealth with eight-figure capital and compute support in a seed funding round to build large-scale, physics-driven data for chemistry and drug discovery. It aims to scale high-fidelity quantum chemistry data and atomistic foundation models to support startups and researchers involved in early-stage drug discovery.
Potential Sciences, a healthtech startup based in Cambridge, has emerged from stealth with eight figures in capital and compute in a seed funding round to build large-scale, physics-driven data for chemistry and drug discovery. The funding will expand its research team, scale its QM1B dataset and train atomistic foundation models designed to predict molecular behaviour before lab assays are run.
Why it matters
Drug discovery AI has largely exhausted the informational value of historical experimental records and low-fidelity simulations. Potential Sciences aims to remove that bottleneck by producing high-quality quantum chemistry data at a scale the field has not previously afforded. If the approach delivers, it could shift early-stage drug decision-making from trial-and-error towards physics-informed predictions, lowering the cost and time needed to rule molecules in or out.
Inside the product
The core technical claim is a reimplementation of CCSD(T) — a gold-standard quantum chemistry method — optimised for GPUs. Traditional CCSD(T) implementations become prohibitively costly as molecule size increases and were built for legacy supercomputers that cannot exploit modern GPU parallelism. Potential Sciences says its engine runs those calculations at roughly 100 times lower cost than conventional codes, enabling accurate data generation for millions of molecules rather than thousands.
That data fuels atomistic foundation models that learn atom-level interactions to predict binding strength, off-target risk and other properties relevant to lead optimisation, reaction prediction and covalent drug design. The company’s team created QM1B, a billion-calculation quantum chemistry dataset, and has competitive wins on large-scale benchmarks: two tracks of the OGB Large-Scale Challenge at NeurIPS 2022. Potential Sciences is staffed by physicists, chemists and mathematicians from Caltech, Cambridge and CERN, and has researchers in Bristol and Tübingen. Advisors include Andrew Fitzgibbon and James Hawkins.
Who are the investors?
Potential Sciences’ seed was led by the Sovereign AI Fund and Twin Path Ventures, with participation from Blue Wire Capital, Lightfield Equity, Nauta Capital, Odyssey, Portfolio Ventures and Rule30. Angel backers include Patrick Collison, co-founder of Stripe, and Chris Gibson, co-founder and former CEO of Recursion; both are known for early-stage bets on deep-technology ventures and, in Recursion’s case, AI-driven drug discovery.
The Sovereign AI Fund is also a compute partner, providing access to national supercomputing via the AI Research Resource, including the Isambard-AI supercomputer in Bristol. Investors say the combined capital and compute arrangement is intended to give the company the practical capacity to scale high-fidelity calculations.
In the announcement, Joséphine Kant, Investment Partner at the Sovereign AI Fund, said:
I first met Hatem in Cambridge, and he immediately struck me as a formidable founder. He combines technical excellence with low ego and unapologetic ambition. He knows the science in extraordinary detail, but he’s also deliberate about bringing in people who are exceptional in areas different from his own. Potential Sciences have the ambition to become a true frontier lab for chemistry, with the potential to fundamentally change how the field works.
In the announcement, Nick Slater, Partner at Twin Path Ventures, said:
Using physics to generate chemistry data only works at scale if it is both fast and cheap enough to run. Hatem and his team have the rare combination of quantum chemistry, GPU engineering and large-scale machine learning needed to make that practical. We think the next step-change in AI drug discovery comes from better data, not just better architectures, and Potential Sciences is building the engine to generate it.
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Founder perspective
Hatem Helal founded Potential Sciences and leads the technical team. The company frames its work as rebuilding quantum chemistry from the equations up so modern hardware can be used efficiently.
In the announcement, Hatem Helal, Founder & CEO at Potential Sciences, said:
AI for drug discovery has learned almost everything the existing experimental record can teach it. The next leap needs new data, and the laws of physics can supply it, as long as your software can use modern hardware. We rebuilt quantum chemistry for GPUs so that gold-standard accuracy is affordable at the scale AI needs. When your code is 100 times more efficient, a seed round buys what used to take a dedicated data centre.
The bigger picture
The deal sits at the intersection of UK strengths in computational science, data infrastructure and drug discovery. Sovereign AI Fund’s compute commitment underlines a wider policy push to give domestic AI teams access to national resources. For the UK healthtech landscape, the combination of tailored compute and specialised datasets reflects an ongoing shift: competitive advantage increasingly depends on owning both high-quality data and the tooling to produce it.
In the announcement, Kanishka Narayan, AI Minister, said:
Potential Sciences is a startup that’s grasping an opportunity Britain is uniquely well-placed to capitalise on: using our deep expertise in data, fundamental science and computing to potentially unleash the next great wave of medical discovery. Solving chemistry’s ‘data bottleneck’ could allow researchers to explore new treatments for diseases that are currently deemed too rare or expensive to focus on. This is AI at its best – pushing the boundaries of innovation, ultimately to improve people’s lives.
If Potential Sciences can deliver on its efficiency and data-scale claims, the outcome would be new, higher-resolution inputs for drug discovery models across the UK and Europe. That would be useful whether in discovering small-molecule leads, improving catalyst and materials design, or supporting academic and industrial chemistry research that has been constrained by compute and data costs.
They've invested in Potential Sciences
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