AQEMIA Achieves Research Milestone Against Undruggable Target in Immuno-Oncology with Servier

AQEMIA, a leading company in generative-AI-driven drug discovery, announces the achievement of a research milestone in its collaboration with Servier, an international independent pharmaceutical company, to address an undruggable target in immuno-oncology.

Based on AQEMIA ?s quantum and statistical physics-based calculations, and within the span of a few months, a series of molecules experimentally active on an undruggable target in immuno-oncology have been successfully identified. This milestone triggers an undisclosed payment to AQEMIA.

"Our teams are very proud of this achievement that demonstrates the power of AQEMIA's cutting-edge generative AI and deep physics in creating novel, patentable molecules to meet therapeutic needs that are still immense, particularly in the field of immuno-oncology. Within the framework of our collaboration with Servier, we have reached the stage where a series of molecules are subsequently proven impactful in vitro and in vivo assays. " said Maximilien Levesque, PhD, CEO and Co-founder of AQEMIA.

This critical milestone is a continuation of Servier and AQEMIA's successful partnership program, initiated in December 2021, to accelerate drug candidate discovery in immuno-oncology using artificial intelligence and deep physics.

AQEMIA has worked on more than a dozen in-house therapeutic programs in oncology and immuno-oncology, as well as in new therapeutic areas such as immunology, inflammation and diseases of the central nervous system. To date, AQEMIA's three most advanced programs are being tested against cancers in animals.

About AQEMIA
AQEMIA is a next-gen techbio generating one of the world's fastest-growing drug discovery pipeline. Our mission is to design fast many innovative drug candidates for critical diseases. Our differentiation lies in our unique quantum and statistical mechanics algorithms to power a generative artificial intelligence designing novel drug candidates, without the need to train on experimental data. We already delivered several drug discovery successes in internal pipeline as well as in collaboration with Pharma companies - most advanced programs being currently in vivo optimization.

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