Aqemia, the Paris-based TechBio company, has been awarded a $7.4 million grant as part of the France 2030 initiative. The non-dilutive funding will be used to advance its RNA-targeting programs and further develop its proprietary generative AI drug discovery platform.
The grant marks a significant step for Aqemia’s expansion into the RNA therapeutics space. By leveraging its physics-enabled AI platform, the company aims to design novel small-molecule drugs for RNA and RNA-modifying targets—therapeutic classes often considered too complex to tackle with conventional approaches.
RNA has long presented both an opportunity and a challenge in drug development. Its highly flexible and structurally dynamic nature makes it difficult to target with traditional small molecules. However, its central role in gene expression and regulation—particularly in cancer—has made RNA an increasingly attractive area for next-generation drug discovery.
Aqemia’s entry into the RNA space reflects a broader trend in the biotech industry: applying advanced computational platforms to previously inaccessible targets. “This funding enables us to extend the reach of our technology to previously unexplored targets, opening the door to new classes of treatments," said Dr. Maximilien Levesque, CEO and Co-Founder of Aqemia.
The newly awarded funds will be used to enhance the adaptability of Aqemia's generative AI technology, particularly in dealing with the complex and flexible structures of RNA. This includes refining the platform’s capabilities through experimental validation of RNA and RNA-modifying targets.
Aqemia also plans to apply these enhancements to improve drug discovery for highly flexible protein targets. One ongoing program—developed in collaboration with Novalix—has already demonstrated promising anti-cancer activity and is currently progressing through in vivo studies.
Founded in Paris, Aqemia combines physics-based algorithms with statistical mechanics to drive generative AI-powered drug discovery. Unlike other AI drug discovery platforms that require vast experimental datasets, Aqemia’s model works without prior experimental data, offering faster, scalable candidate generation.
The company is currently running multiple internal and partnered drug discovery programs, several of which have reached preclinical validation. Its AI engine is designed to simulate molecular interactions rapidly and accurately, positioning Aqemia as a potential leader in the computational drug discovery field.
