Traditional drug discovery is slow and expensive and relies heavily on trial and error. Aqemia combines generative AI and quantum-inspired physics algorithms to accelerate and improve the development of new drug candidates. Their technology often identifies potential drug molecules faster than conventional methods, reducing discovery and preclinical phases from 5 to 6 years to under two years.
Through the France 2030 plan, the company has secured a $7.4 million grant to enhance its generative AI platform’s capability to work with highly flexible proteins and RNA targets. To improve its technology, the company will conduct experimental studies on various RNA and RNA-modifying targets.
“This funding enables us to extend the reach of our technology to previously unexplored targets, opening the door to new classes of treatments. Integrating RNA targeting into our platform reinforces our ambition to transform the invention of new therapeutic solutions for patients. We are grateful for the continued support that allows us to push boundaries and tackle critical medical challenges,” said Dr. Maximilien Levesque, CEO and Co-Founder of Aqemia.
The new funding brings Aqemia’s total capital to $125.4M, though the valuation remains undisclosed. In December 2024, the company raised €36M from Elaia Partners, BPifrance, Eurazeo, Cathay Innovation, and Wendel.
Building one of the world’s fastest-growing drug discovery pipelines
Aqemia emerged in 2019 as a spin-off from École Normale Supérieure (ENS) in Paris, founded by Maximilien Levesque and Emmanuelle Martiano Rolland. The venture grew from Levesque’s quantum and statistical physics research at CNRS, where he discovered that unresolved equations could aid drug discovery. Together with Martiano, a CentraleSupélec graduate and strategy consultant, they transformed this insight into a startup aimed at revolutionising pharmaceutical development.
The company develops therapeutic molecules for diseases with unmet medical needs. Through internal projects and partnerships with major pharmaceutical companies like Sanofi, it addresses complex challenges such as selectivity and limited chemical data.
“We have achieved several drug discovery breakthroughs in our internal initiatives and collaborations with leading pharmaceutical firms. Our primary programs have demonstrated efficacy in animal models for cancer,” states the company overview.
Unlike traditional methods, Aqemia generates data through physics-based computations rather than relying on experimental datasets, enabling faster and more cost-effective drug development. The company accelerates drug discovery through its unique combination of generative AI and quantum-inspired physics algorithms. They focus on finding innovative drug candidates for urgent medical needs, especially in oncology, immunology, inflammation, and central nervous system disorders.
How Aqemia targets RNAs using its physics-enabled generative AI engine
RNAs are complex therapeutic targets due to their adaptable structures. Their crucial role in gene regulation and expression, especially in cancer, makes them valuable targets for innovative drug discovery.
Unlike traditional AI platforms that need extensive experimental data, Aqemia’s quantum-inspired algorithms simulate molecular interactions 10,000 times faster than conventional methods by solving Schrödinger-like equations from statistical mechanics. This generates synthetic data de novo, allowing AI models to explore chemical possibilities free from historical constraints while effectively targeting flexible RNA structures.
Aqemia has advanced several RNA-focused drug discovery programs toward clinical trials. Working with Novalix, one program shows promising effects on cancer cells and is progressing through animal studies. This program targets RNA-modifying enzymes that help tumors evade immune responses, an innovative approach made possible by Aqemia’s physics-driven AI engine modeling RNA- ligand interactions at the atomic scale.
Aqemia is enhancing its computational platform to target RNAs using its physics-driven generative AI engine. Drawing on its epitranscriptomics achievements, they aim to develop innovative small-molecule drugs for this promising new class of therapeutic targets. While advancing RNA-focused projects, they maintain progress on their broader pipeline, including protein-targeting programs.
In January 2025, Aqemia opened a London office to tap into UK computational biology talent while complementing its Paris-based quantum physics expertise. As it expands its RNA-focused and protein-targeting initiatives, it plans to double its workforce by 2026.
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