P
Petra Schneider
Researcher at ETH Zurich
Publications - 89
Citations - 4321
Petra Schneider is an academic researcher from ETH Zurich. The author has contributed to research in topics: Drug discovery & Virtual screening. The author has an hindex of 31, co-authored 85 publications receiving 3399 citations. Previous affiliations of Petra Schneider include École Polytechnique Fédérale de Lausanne.
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Counting on natural products for drug design
TL;DR: This work highlights the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempts to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.
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Rethinking drug design in the artificial intelligence era
Petra Schneider,W. Patrick Walters,Alleyn T. Plowright,Norman Sieroka,Jennifer Listgarten,Robert Alan Goodnow,Jasmin Fisher,Jasmin Fisher,Johanna M. Jansen,José S. Duca,Thomas S. Rush,Matthias Zentgraf,John Edward Hill,Elizabeth Krutoholow,Matthias Kohler,Jeff Blaney,Kimito Funatsu,Kimito Funatsu,Chris Luebkemann,Chris Luebkemann,Gisbert Schneider +20 more
TL;DR: The views of a diverse group of international experts on the ‘grand challenges’ in small-molecule drug discovery with AI are presented, including obtaining appropriate data sets, generating new hypotheses, optimizing in a multi-objective manner, reducing cycle times and changing the research culture.
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Generative Recurrent Networks for De Novo Drug Design.
Anvita Gupta,Anvita Gupta,Alex T. Müller,Berend J. H. Huisman,Jens A. Fuchs,Petra Schneider,Gisbert Schneider +6 more
TL;DR: This paper presents a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short‐term memory (LSTM) cells that captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy.
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Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.
TL;DR: SPiDER is presented, a unique technique that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds, and discovered a potential off-target liability of fenofibrate-related compounds.
Journal ArticleDOI
Dual-display of small molecules enables the discovery of ligand pairs and facilitates affinity maturation
Moreno Wichert,Nikolaus Krall,Willy Decurtins,Raphael M. Franzini,Francesca Pretto,Petra Schneider,Dario Neri,Jörg Scheuermann +7 more
TL;DR: The discovery of a low micromolar binder to alpha-1-acid glycoprotein and the affinity maturation of a ligand to carbonic anhydrase IX, an established marker of renal cell carcinoma are reported, which dramatically improved tumour targeting performance in vivo.