Public Lecture: Ernst Schering Preis

On Sept. 29, 2022, the public lecture of the "Ernst Schering Prize for Outstanding Research in Basic Biomedical Research" winner will take place at ECDF. The Ernst Schering Prize, endowed with 50,000 euros, is one of the most prestigious German science prizes. It was established in 1991 by the Ernst Schering Research Foundation and has been awarded annually by the Ernst Schering Foundation since 2003. The prize is awarded to scientists worldwide whose groundbreaking research work has produced new inspiring models or fundamental changes in knowledge in the field of biomedicine. In particular, the Foundation would like to honor scientists who, in addition to their cutting-edge research in the fields of biology, medicine or chemistry, are actively engaged in debates relevant to society or have launched targeted initiatives that inspire and support future generations of scientists. This year's laureate is Professor Gisbert Schneider, who will give a public talk on the topic "De novo drug design with machine intelligence". The lecture is aimed at scientists and students. Lecture in English. Registration is not required. The lecture will start at 10:30 am


In cooperation with the Einstein Center Digital Future the Schering Stiftung presents a scientific lecture by this year’s Ernst Schering Prize laureate Professor Gisbert Schneider.

Molecular design may be regarded as a pattern recognition process. In this context, certain machine learning methods have emerged as an enabling technology for modern drug discovery. These models aim to mimic a chemist’s pattern recognition skills by learning from domain–specific data. Part of the appeal of applying “artificial intelligence” (AI) to drug design lies in the potential to develop autonomous models that navigate vast molecular datasets and prioritize alternatives. This concept of drug discovery represents at least a partial transfer of decision power to an AI, and could be viewed as synergistic with human intelligence; that is, a domain-specific implicit AI that would augment the capabilities of chemists in molecular design and selection. More ambitiously, the ultimate challenge for drug design with AI is to autonomously generate new chemical entities with the desired properties from scratch (“de novo”). This approach largely eliminates the need for the often prohibitively costly, serendipitous experimental chemical compound testing. I will present knowledge-based and data–driven methods for de novo molecule generation and pharmacological activity prediction, emphasizing ligand-based approaches that have proven useful and reliable in “little–data” scenarios. Selected prospective case studies will be presented, ranging from targeted molecular design to fully automated design-make-test-analyze cycles.