Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Christoph Grebner"'
Publikováno v:
Frontiers in Chemistry, Vol 10 (2022)
The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constrai
Externí odkaz:
https://doaj.org/article/738f3a890d1a4b8088e6483504358356
Autor:
Friedemann Schmidt, Christoph Grebner, Daniel Kofink, Jan Wenzel, Gerhard Hessler, Hans Matter
Publikováno v:
ChemMedChem. 16:3772-3786
In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a25ab258232272a991457e2f08c15f1c
https://doi.org/10.1002/cmdc.202100418
https://doi.org/10.1002/cmdc.202100418
Autor:
Maxime Langevin, Christoph Grebner, Stefan Guessregen, Susanne Sauer, Yi Li, Hans Matter, Marc Bianciotto
Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proofs of concepts already published. Nevertheless, generative models are known for sometimes generating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20781935bb61806be075c759e35f3036
https://doi.org/10.26434/chemrxiv-2022-mdhwz
https://doi.org/10.26434/chemrxiv-2022-mdhwz
Publikováno v:
Journal of Medicinal Chemistry. 63:8809-8823
Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8bdca7bf8e10622e6d7c476536b3ee8
https://doi.org/10.1021/acs.jmedchem.9b02044
https://doi.org/10.1021/acs.jmedchem.9b02044
Publikováno v:
Journal of chemical information and modeling. 62(3)
In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14fb1baed499dda56ecbb6da5cfca9a1
https://pubmed.ncbi.nlm.nih.gov/35080887
https://pubmed.ncbi.nlm.nih.gov/35080887
Publikováno v:
Artificial Intelligence in Drug Design ISBN: 9781071617861
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06d3f1ab5e683ed791d279d17eccf199
https://doi.org/10.1007/978-1-0716-1787-8_15
https://doi.org/10.1007/978-1-0716-1787-8_15
Publikováno v:
Methods in molecular biology (Clifton, N.J.). 2390
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::83aff5625f58a27018dadf96d08be145
https://pubmed.ncbi.nlm.nih.gov/34731477
https://pubmed.ncbi.nlm.nih.gov/34731477
Publikováno v:
Target Discovery and Validation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::775095aff31dd4659d26e56b694489ba
https://doi.org/10.1002/9783527818242.ch10
https://doi.org/10.1002/9783527818242.ch10
Autor:
Christian Tyrchan, Robert Soliva, Martí Municoy, Oriol Gracia Carmona, Martin J. Packer, Daniel Soler, Joan Gilabert, Victor Guallar, Anders Hogner, Samantha Jayne Hughes, Christoph Grebner, Daniel Lecina
Publikováno v:
Journal of Chemical Theory and Computation. 15:6243-6253
In this study, we present a fully automatic platform based on our Monte Carlo algorithm, the Protein Energy Landscape Exploration method (PELE), for the estimation of absolute protein-ligand binding free energies, one of the most significant challeng
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b0432aa69aaf988f74cc130d1d7a632
https://doi.org/10.1021/acs.jctc.9b00753
https://doi.org/10.1021/acs.jctc.9b00753
Autor:
Louis Leong, Benjamin Tehan, Dean G. Brown, Dawn M. Troast, Giles A. Brown, Nils-Olov Hermansson, Peter E. Thornton, Cédric Fiez-Vandal, Holly H. Soutter, Oliver Schlenker, Fiona H. Marshall, Robert M. Cooke, Arjan Snijder, Karl Edman, Christoph Grebner, Giselle R. Wiggin, A.S. Dore, Stefan Geschwindner, Andrei Zhukov, Birte Aggeler, Christoph E. Dumelin, Niek Dekker, Patrik Johansson, Robert K. Y. Cheng, Mathieu Rappas, Ali Jazayeri, Linda Sundström, Rudi Prihandoko
Publikováno v:
Nature. 545:112-115
Crystal structures of protease-activated receptor 2 (PAR2) in complex with two different antagonist ligands and with a blocking antibody reveal binding sites that are distinct from those found on PAR1, offering new leads for structure-based drug desi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60ed03d801965b48c5792271544c4222
https://doi.org/10.1038/nature22309
https://doi.org/10.1038/nature22309