SPP 2363 on “Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning”

From Fundamentals to Application and Beyond
“Today the computer is just as important as tool for chemists as the test tube. Simulations are so realistic that they predict the outcome of traditional experiments.”
The Royal Swedish Academy of Science, 2013

SPP 2363 officially started

The DFG has selected suitable projects and the official funding letters were received. Let' get started, everyone!
Interested students, kindly contact the respective principal investigators.

© 2022 American Chemical Society

In C&EN's June cover story, Matthias Rarey highlights how computational tools help to navigate chemical spaces and virtual libraries in the search for new drugs.

Connecting chemical building blocks allows drug hunters to explore a much bigger chemical space than before. The challenge is to narrow this field of compounds to something manageable. To do that, chemists are turning to new computational tools to navigate this increasingly huge chemical universe, and they are combining technologies. Experts say these new approaches should speed up the identification process, and industry is investing time and money to optimize the hunt.

“We must learn to understand chemistry as data science”

Frank Glorius and Philipp Pflüger talk about the new field of research “Molecular Machine Learning".

“Molecular Machine Learning” (MML) is a new branch of research with the potential to change chemical research. Prof. Frank Glorius, coordinator of the new Priority Programme “Molecular Machine Learning” (SPP 2363), funded by the German Research Foundation (DFG), and Philipp Pflüger, who is working on his PhD in Chemistry and helped to develop the programme, explain in this interview with Christina Hoppenbrock what MML means, what opportunities and challenges this new field of research presents, and what working in chemistry will be like in tomorrow’s world.

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