“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.”
Yesterday, the fifth edition of the international symposium series on "Molecular Machine Learning" took place virtually. Over 200 participants joined the symposium and made it yet again a very special event. The invited speakers Núria López (ICIQ), Kim Jelfs (Imperial College London), Tim Cernak (University of Michigan) and Sarah Reisman (Caltech) gave fascinating insights into their research and highlighted how data-driven approaches can be utilized for computer-aided synthesis planning, molecular design, automation and drug discovery.
The fifth edition of the international symposium series on "Molecular Machine Learning" organized by Prof. Frank Glorius will take place on January 19th, 2023 from 3:00 pm. The virtual conference series brings together leading scientists from fields including computer-aided synthesis planning, data-driven molecular design, automation and AI-enabled drug discovery. Speakers at the symposium will be: Núria López (ICIQ), Kim Jelfs (Imperial College London), Tim Cernak (University of Michigan) and Sarah Reisman (Caltech).
The SPP 2363 Kick-off Symposium took place at the German National Academy of Sciences Leopoldina in Halle from October 17th to October 21st. With PhD talks and posters on the various applications of molecular machine learning anchored in the priority program and the opportunity to build an even stronger the community, the event was a great success. Talks from industry and researcher provided a great setting for exchanging ideas and setting goals for the future development of the program. We were also very pleased that Tiago Rodrigues (University of Lisbon) accepted our invitation and joined the symposium as the first SPP 2363 Mercator Fellow. The event was supported by the German National Academy of Sciences Leopoldina and the DFG.
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.
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.
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.
