The Gmelin-Beilstein Memorial Medal 2022 has been awarded to Professor Gisbert Schneider, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland, by the Gesellschaft Deutscher Chemiker (GDCh, German Chemical Society). The prize was presented at the 17th German Conference on Cheminformatics, Garmisch-Partenkirchen, on May 9, 2022.
The award honors individuals for their contributions to the history of chemistry, chemical literature, or chemical information, and comes with EUR 7,500 of prize money. Schneider is a well-known researcher in the field of chemoinformatics. His research focuses on the development and application of adaptive intelligent systems for molecular design and drug discovery. Schneider is honored for his pioneering work in integrating machine learning methods into practical medicinal chemistry.
Gisbert Schneider, born in 1965, studied biochemistry at the Free University Berlin, Germany, where he completed his Ph.D. in 1994. After postdoctoral research at the Benjamin Franklin University Clinic, Berlin, the Massachusetts Institute of Technology (MIT), Cambridge, USA, and the Max Planck Institute for Biophysics, Frankfurt am Main, Germany, he gained industrial experience at Hoffmann–La Roche AG, Basel, Switzerland.
Schneider completed his habilitation in biochemistry and bioinformatics at the Albert-Ludwigs-University Freiburg, Germany, in 2000, where he subsequently worked as a Lecturer. From 2002 to 2009, he held the Beilstein Endowed Chair for Chem- and Bioinformatics at the University of Frankfurt am Main. He was appointed Professor for Computer-Assisted Drug Design at ETH Zurich in 2010. From 2018 to 2020, Schneider also served as Associate Vice President of ETH Global. He has also been Director of the Singapore-ETH Center in Singapore since 2021.
Among other awards, Gisbert Schneider has received the Herman Skolnik Award from the Division of Chemical Information of the American Chemical Society (ACS) in 2018 and the Prous Institute – Overton and Meyer Award for New Technologies in Drug Discovery in 2020. In 2014, he was named a “Highly Cited Researcher” by Thomson Reuters.
Selected Publications
- Δ-Quantum machine-learning for medicinal chemistry,
Kenneth Atz, Clemens Isert, Markus N. A. Böcker, José Jiménez-Luna, Gisbert Schneider,
Phys. Chem. Chem. Phys. 2022.
https://doi.org/10.1039/d2cp00834c - Perplexity-Based Molecule Ranking and Bias Estimation of Chemical Language Models,
Michael Moret, Francesca Grisoni, Paul Katzberger, Gisbert Schneider,
J. Chem. Inf. Model. 2022, 62, 1199–1206.
https://doi.org/10.1021/acs.jcim.2c00079 - Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence,
Michael Moret, Moritz Helmstädter, Francesca Grisoni, Gisbert Schneider, Daniel Merk,
Angew. Chem. Int. Ed. 2021, 60, 19477–19482.
https://doi.org/10.1002/anie.202104405 - Geometric deep learning on molecular representations,
Kenneth Atz, Francesca Grisoni, Gisbert Schneider,
Nat. Mach. Intell. 2021, 3, 1023–1032.
https://doi.org/10.1038/s42256-021-00418-8 - Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (−)‐Englerin A Mimetics,
Lukas Friedrich, Ryan Byrne, Aaron Treder, Inderjeet Singh, Christoph Bauer, Thomas Gudermann, Michael Mederos y Schnitzler, Ursula Storch, Gisbert Schneider,
ChemMedChem 2020, 15, 566–570.
https://doi.org/10.1002/cmdc.202000017 - Counting on natural products for drug design,
Tiago Rodrigues, Daniel Reker, Petra Schneider, Gisbert Schneider,
Nat. Chem. 2016, 8, 531–541.
https://doi.org/10.1038/NCHEM.2479 - Deep Learning in Drug Discovery,
Erik Gawehn, Jan A. Hiss, Gisbert Schneider,
Mol. Inf. 2016, 35, 3–14.
https://doi.org/10.1002/minf.201501008 - Designing antimicrobial peptides: form follows function,
Christopher D. Fjell, Jan A. Hiss, Robert E. W. Hancock, Gisbert Schneider,
Nat. Reviews Drug Discov. 2011, 11, 37–51.
https://doi.org/10.1038/nrd3591 - Virtual screening: an endless staircase?,
Gisbert Schneider,
Nat. Rev. Drug Discov. 2010, 9, 273–276.
https://doi.org/10.1038/nrd3139 - Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning,
Dominique Bruns, Erik Gawehn, Karthiga Santhana Kumar, Petra Schneider, Martin Baumgartner, Gisbert Schneider,
ChemBioChem 2019, 21, 500–507.
https://doi.org/10.1002/cbic.201900346
