Schering Stiftung

Lecture 

Gisbert Schneider in conversation with a lab member and their “colleague computer.” The AI software developed by Schneider’s MODLAB team provides valuable suggestions for drug research. The intensive scientific discussions are an important precondition for gaining knowledge.

Gisbert Schneider in conversation with a lab member and their “colleague computer.” The AI software developed by Schneider’s MODLAB team provides valuable suggestions for drug research. The intensive scientific discussions are an important precondition for gaining knowledge.
Photo: Daniel Winkler / ETH Zurich

Gisbert Schneider’s interdisciplinary working group at ETH Zurich brings together researchers from the computer sciences and the engineering and natural sciences.

Gisbert Schneider’s interdisciplinary working group at ETH Zurich brings together researchers from the computer sciences and the engineering and natural sciences.
Photo: Daniel Winkler / ETH Zurich

Gisbert Schneider: De novo drug design with machine intelligence

Public Scientific Lecture

Gisbert Schneider: De novo drug design with machine intelligence

Public Scientific Lecture

Date:

September 29, 2022, 10:30 a.m.

Venue:

Einstein Center Digital Future
Wilhelmstraße 67,
10117 Berlin

The lecture is aimed at scientists and students.

Lecture in English. Registration is not required.

 


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.

The lecture is aimed at a scientific audience.

Prof. Dr. Gisbert Schneider

Prize winner

Gisbert Schneider is Professor of Computer-Assisted Drug Design at the Institute of Pharmaceutical Sciences at ETH Zurich. He is considered a pioneer in the field of targeted molecule design using machine learning method

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Ernst Schering Prize Lecture 2022

Lecture — September 29, 2022

Prof. Dr. Gisbert Schneider presents his research in the field of molecule design and the development of machine learning methods to predict drug activity.

Learn more

Award Ceremony 2022

Award ceremony — September 29, 2022

The Schering Stiftung awards the Ernst Schering Prize to the biochemist and bioinformatician Gisbert Schneider. The physician and research group leader Sarah Kim-Hellmuth will be awarded with the Friedmund Neumann Prize.

Learn more

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Public Scientific Lecture

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Partners 

This Project is realized in cooperation with the following partners:

Einstein Center

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