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Digital & Innovation

UCB integrates innovative AI system into drug discovery programs

Health Industry Hub | March 12, 2021 |

Digital & Innovation: UCB is integrating an artificial intelligence (AI) company’s technology into its internal drug discovery pipeline.

UCB’s early adoption of Insilico Medicine’s proprietary technology will provide UCB’s scientists with the ability to design novel hit compounds that satisfy multiple predefined parameters rapidly and streamline lead optimization.

With the Chemistry42™ platform, UCB scientists will also reduce the efforts and costs typically associated with the design, testing and commercialisation of new drugs. Chemistry42™ v1.0 will be customised and deployed on UCB’s cloud-based supercomputing infrastructure.

“Insilico Medicine has been very responsive in this quickly developing area of science and we are delighted to be one of the launch partners for Chemistry42,” Jiye Shi, Global Head of Computer-Aided Drug Design at UCB said. “Our hope is that this platform will further enhance the digital transformation of our drug discovery capabilities for the benefit of patients worldwide.”

“UCB is one of the leading companies in small molecule drug discovery. It was their insight and approach at the forefront of this science that encouraged us to move into AI-powered chemistry over five years ago and it gives me great pleasure to see UCB among the launch partners of our Chemistry42 operating system,” said Alex Zhavoronkov, Ph.D., CEO of Insilico Medicine.

In November 2020, Merck became Insilico Medicine’s first launch partner for this AI platform. 

Chemistry42™ is a flexible, user-friendly software platform that integrates artificial intelligence (AI) methods with the fields of medicinal and computational chemistry for the design of novel small molecules with user-defined druglike physicochemical properties. The platform is a scalable distributed web application capable of running multiple tasks in parallel in a matter of hours. Container orchestration and workflow management allow for predictable hardware-agnostic resource allocation and the implementation on either cloud or local HPC infrastructures.


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