G
Gisbert Schneider
Researcher at ETH Zurich
Publications - 483
Citations - 24401
Gisbert Schneider is an academic researcher from ETH Zurich. The author has contributed to research in topics: Virtual screening & Pharmacophore. The author has an hindex of 68, co-authored 482 publications receiving 20364 citations. Previous affiliations of Gisbert Schneider include Max Planck Society & Charité.
Papers
More filters
Journal ArticleDOI
Designing antimicrobial peptides: form follows function
TL;DR: In this article, advanced computer assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.
Journal ArticleDOI
Computer-based de novo design of drug-like molecules
Gisbert Schneider,Uli Fechner +1 more
TL;DR: This review outlines the various design concepts and highlights current developments in computer-based de novo design of hit and lead structure candidates for drug discovery projects.
Journal ArticleDOI
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.
Journal ArticleDOI
“Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening
TL;DR: A chemically advanced template search based on topological pharmacophore models has been developed as a technique for virtual screening and has successfully identified novel potent Ca(2+) antagonists in a library of several hundred thousand compounds on the basis of a correlation vector representation.
Journal ArticleDOI
Deep Learning in Drug Discovery.
TL;DR: An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.