Merck and Mulliken Center for Theoretical Chemistry enter into cooperation on machine learning

Merck and Mulliken Center for Theoretical Chemistry enter into cooperation on machine learning

Merck has announced a collaboration with the Mulliken Center for Theoretical Chemistry at the Rheinische Friedrich-Wilhelms-Universität Bonn. The three-year collaborative program will focus on developing new tools for computational chemical modeling and new representations of molecules to drive the next generation of molecular machine learning.

“We are very excited to be working with Merck on this project, which will benefit not only the company but also the scientific community in the field of computational chemistry. The close collaboration with the scientists from Merck will help us to give the project and the developed tools the right focus,” said Prof. Stefan Grimme, head of the Mulliken Center for Theoretical Chemistry, member of the National Academy of Sciences Leopoldina and internationally renowned researcher in the field of theoretical chemistry. Grimme’s research group has developed countless methods and tools that are now widely used, even beyond the field of computational chemistry.

Merck uses machine learning and artificial intelligence (AI) in all phases of its value chain. With numerous initiatives and cooperations, the company wants to accelerate the life cycle of its products, break down silos and tap the potential of data and digitization. “Recent advances have shown the impact molecular machine learning and AI can have on all areas of chemistry more generally, particularly simulation and data-driven drug discovery, materials design, and the prediction of new formulations. With this collaboration, we aim to jointly develop new representations of molecules and computational tools that will help us accelerate the screening of drug candidates, discover new compounds and predict the performance of materials,” said Jan Gerit Brandenburg, Head of Digital Chemistry at Merck .

Over the next three years, several PhD students from the Mulliken Center for Theoretical Chemistry will work with Merck’s Digital Chemistry team to identify methods that are applicable to Merck’s entire chemical and pharmaceutical portfolio and will benefit from molecular machine learning techniques. All methods and code developed within the framework of the program are open source and thus also useful for the general scientific community. The program is partly embedded in the priority program “Molecular Machine Learning” (SPP 2363) of the German Research Foundation (DFG).

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