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Cyrill Brunner

Researcher at ETH Zurich

Publications -  16
Citations -  146

Cyrill Brunner is an academic researcher from ETH Zurich. The author has contributed to research in topics: Cancer research & Fibroblast growth factor receptor. The author has an hindex of 4, co-authored 12 publications receiving 85 citations. Previous affiliations of Cyrill Brunner include École Polytechnique Fédérale de Lausanne.

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When barriers ignore the “rule-of-five”

TL;DR: Experimental data with liposomes and molecular dynamics simulations suggest that the formation of intramolecular H-bonds and the possibility to adopt an elongated shape besides the presence of a significant fraction of net neutral species facilitate lipid bilayer permeation.
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De Novo Fragment Design for Drug Discovery and Chemical Biology.

TL;DR: The study validates computational de novo design as a prime method for generating chemical probes and starting points for drug discovery and identifies additional macromolecular targets of the computationally designed compound and the structurally related marketed drug azosemide.
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A novel FRET peptide assay reveals efficient Helicobacter pylori HtrA inhibition through zinc and copper binding.

TL;DR: A novel fluorescence resonance energy transfer (FRET) peptide derived from a gel-based label-free proteomic approach is designed as a valuable substrate for H. pylori HtrA to contribute to the development of novel metal ion-dependent protease inhibitors, which might help to fight bacterial infections.
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Binding Specificities of Nanobody•Membrane Protein Complexes obtained from Chemical Cross-linking and High- Mass MALDI Mass Spectrometry

TL;DR: It is shown how chemical cross-linking combined with high-mass matrix-assisted laser/desorption ionization mass spectrometry can be employed as a fast screening technique to determine binding specificities of intact nanobody•membrane protein complexes.
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Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

TL;DR: In this paper , a collection of virtual molecules was created with a generative chemical language model (CLM) for de novo molecular structure generation by learning from a textual representation of molecules.