Graph Therapeutics Raises €5M to Build AI-Driven Drugs After Exscientia's $60M Allcyte Acquisition

From a $60M Acquisition to Building Their Own Drugs — Meet Graph Therapeutics

In AI drug discovery Europe, a new name is making waves. Graph Therapeutics, founded by the same scientific team behind Allcyte — the Vienna-based drug testing company acquired by Exscientia for $60 million in June 2021 — has raised $5 million in seed funding led by French venture capital firm Daphni. The raise signals a bold next chapter: rather than powering other companies' drug pipelines, the team now wants to build medicines of their own.

Allcyte made its name with a specific, powerful idea: that testing drugs directly on a patient's own cancer cells — rather than relying on genetic sequencing alone — could predict treatment outcomes far more reliably. Exscientia, the Oxford-based AI drug discovery firm, saw enough potential in this functional testing approach to pay a significant premium for the company. Now, with that proof of concept validated by a major acquisition and years of additional research, the founding team has re-emerged with an even more ambitious mission.

What Does Graph Therapeutics Actually Build?

Graph Therapeutics is positioning itself at the intersection of artificial intelligence and functional biology — a space that is drawing increasing attention from investors and pharmaceutical companies alike. The company's approach combines AI-driven drug design with real-world cellular testing to identify and develop novel therapeutic compounds, particularly in oncology.

Unlike purely computational drug discovery platforms, which rely on molecular simulations and genomic data to predict how a drug might behave, Graph Therapeutics anchors its AI models in experimental data derived from actual patient-derived cells. This hybrid approach — sometimes called functionally-informed AI drug discovery — aims to reduce the notoriously high failure rate of drugs in clinical trials, where the majority of compounds that look promising in silico or in animal models still fail when tested in humans.

According to research published in Nature Reviews Drug Discovery, the overall probability of a drug successfully completing clinical trials remains below 10%, a figure that has barely shifted despite decades of investment in genomics and molecular biology. Functional testing platforms like the one developed by Allcyte — and now refined at Graph Therapeutics — represent one of the more credible attempts to change that equation.

Why Daphni Bet on This Team — and What It Tells Us About European Biotech VC

Daphni is a Paris-based venture capital firm known primarily for backing European tech startups, with a portfolio that spans deep tech, software, and increasingly, life sciences. Its decision to lead the Graph Therapeutics seed round reflects a broader shift in European VC strategy: investors who cut their teeth on SaaS and platform businesses are now moving aggressively into biotech, particularly where AI is a differentiating factor.

This trend is documented extensively in European Investment Bank reports on venture capital activity, which have consistently highlighted AI-driven life sciences as one of the fastest-growing segments in European startup investment. For a VC like Daphni, backing a team with a proven track record — one that built a company valuable enough for Exscientia to pay $60 million for it — reduces the foundational risk that makes early-stage biotech so challenging.

The logic is sound from a European competitiveness standpoint as well. The continent has long struggled to retain deep tech talent once companies reach a certain scale — a pattern sometimes called "the European exit trap," where promising startups get acquired by US or Asian firms before they can mature into independent global players. Graph Therapeutics, by raising capital to build its own drug pipeline rather than selling technology services, is explicitly choosing a different path.

The broader European AI regulation landscape, including the EU AI Act, also plays a role here. AI systems used in clinical decision-making or drug discovery will face significant regulatory scrutiny under the Act's high-risk classification framework. Teams with deep experience navigating both the scientific and regulatory dimensions of AI in medicine — as the Allcyte founders clearly have — will carry a distinct advantage. For more context on how AI regulation is shaping European health tech, MIT Technology Review has covered the evolving compliance landscape in detail.

The Allcyte Playbook: How Functional Drug Testing Became a $60M Idea

To understand what Graph Therapeutics is building, it helps to revisit what made Allcyte valuable enough for Exscientia to acquire it in June 2021 for $60 million. Allcyte's platform was built around a deceptively simple premise: before deciding which drug to give a cancer patient, test multiple drug compounds directly on that patient's own cancer cells in the lab, observe how those cells actually respond, and use that functional data to guide treatment decisions.

This stands in contrast to the dominant paradigm in precision oncology at the time, which leaned heavily on genomic sequencing — identifying mutations in a patient's DNA and then matching those mutations to targeted therapies. While genomic approaches have produced genuine breakthroughs (BRCA mutations and PARP inhibitors being a well-known example), they do not capture the full complexity of how a patient's cancer will actually respond to treatment. Many patients with "actionable" mutations still fail to respond to the corresponding targeted drugs.

Allcyte's functional testing approach addressed this gap directly. By the time Exscientia acquired the company, Allcyte had built a substantial dataset linking drug responses in patient-derived cells to real-world clinical outcomes — exactly the kind of proprietary, high-quality training data that AI drug discovery models need to move beyond theoretical predictions.

Exscientia itself was a pioneer in applying AI to drug discovery — its platform claimed to design drug candidates significantly faster than traditional methods, and the company attracted major pharma partnerships before eventually going public and then private again. Integrating Allcyte's functional data layer into that workflow was a logical extension. But for the Allcyte founders, working within a larger organisation's roadmap appears to have been a temporary stop rather than a final destination.

AI Drug Discovery in Europe: A Market That Is Growing Faster Than Regulation Can Keep Up

The market context for Graph Therapeutics' raise is striking. AI drug discovery has evolved from a speculative research area into a genuine commercial sector over the past several years. According to Statista market analysis, the global AI in drug discovery market is projected to grow substantially through the coming decade, driven by the compounding cost pressures of traditional pharmaceutical R&D, advances in large language models and molecular AI, and the growing availability of high-quality biological datasets.

Europe has been staking out a distinct position in this landscape. While US firms like Recursion Pharmaceuticals and Schrödinger dominate in terms of public market capitalisation, European startups — particularly those emerging from strong academic ecosystems in the UK, Austria, Germany, and the Netherlands — are increasingly competitive at the research and early clinical stages. The region's strength in regulatory science, combined with the EU's early-mover advantage in establishing AI governance frameworks, creates a compelling environment for companies navigating the complex approval pathways that AI-designed drugs must eventually travel.