NovAliX partners with Chemical.AI on drug discovery AI tookit

The program focuses on enhancing Chemical.AI's computer-aided synthesis planning (CASP) system, to improve its predictive retrosynthesis

NovAliX has announced a collaboration with Chemical.AI in drug discovery.

The partners aim to to jointly develop an AI toolkit for drug discovery. The toolkit will be integrated into NovAliX's drug discovery services while commercial release will be available at Chemical.AI.

Through the collaboration, NovAliX will have access to a customised and enhanced toolkit ahead of the market. Chemical.AI will be able to rely on NovAliX's scientists and data to develop the AI tools. The program focuses on enhancing Chemical.AI's computer-aided synthesis planning (CASP) system, to improve its predictive retrosynthesis.

CASP offers the potential, the companies say, to significantly reduce the attrition of synthetic chemistry and increase efficiency and productivity, by accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input, and then output a sorted list of detailed reaction schemes that each connect a target to purchasable starting materials via a series of chemically feasible reaction steps.

“We are thrilled to collaborate with Chemical.AI at this significant and impactful time for us,” said Denis Zeyer, CEO of NovAliX. “With this collaboration, NovAliX and Chemical.AI have launched a research program aimed at demonstrating the performance of Chemical.AI’s CASP system. As a result of this teamwork, NovAliX clients will benefit from cutting-edge AI solutions for their research projects. For example, the most advanced version of Chemical.AI’s CASP is already used by NovAliX chemists in discovery chemistry and process research services.”

“The decision to collaborate with NovAliX was a natural move for us,” said Ning Xia, PhD, CEO of Chemical.AI. “First, we wanted to team up with scientists who need to solve very complex synthetic challenges. Second, we were seeking exposure to scientists who embrace the variety of preclinical drug discovery projects that enhance the development of the relevant AI solutions for the pharmaceutical industry.”

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