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Large-scale integrated super-computing platform for next generation virtual drug discovery
Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacologica...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108376/ https://www.ncbi.nlm.nih.gov/pubmed/21723773 http://dx.doi.org/10.1016/j.cbpa.2011.06.005 |
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author | Mitchell, Wayne Matsumoto, Shunji |
author_facet | Mitchell, Wayne Matsumoto, Shunji |
author_sort | Mitchell, Wayne |
collection | PubMed |
description | Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand–receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed. |
format | Online Article Text |
id | pubmed-7108376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71083762020-03-31 Large-scale integrated super-computing platform for next generation virtual drug discovery Mitchell, Wayne Matsumoto, Shunji Curr Opin Chem Biol Article Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand–receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed. Elsevier Ltd. 2011-08 2011-06-30 /pmc/articles/PMC7108376/ /pubmed/21723773 http://dx.doi.org/10.1016/j.cbpa.2011.06.005 Text en Copyright © 2011 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Mitchell, Wayne Matsumoto, Shunji Large-scale integrated super-computing platform for next generation virtual drug discovery |
title | Large-scale integrated super-computing platform for next generation virtual drug discovery |
title_full | Large-scale integrated super-computing platform for next generation virtual drug discovery |
title_fullStr | Large-scale integrated super-computing platform for next generation virtual drug discovery |
title_full_unstemmed | Large-scale integrated super-computing platform for next generation virtual drug discovery |
title_short | Large-scale integrated super-computing platform for next generation virtual drug discovery |
title_sort | large-scale integrated super-computing platform for next generation virtual drug discovery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7108376/ https://www.ncbi.nlm.nih.gov/pubmed/21723773 http://dx.doi.org/10.1016/j.cbpa.2011.06.005 |
work_keys_str_mv | AT mitchellwayne largescaleintegratedsupercomputingplatformfornextgenerationvirtualdrugdiscovery AT matsumotoshunji largescaleintegratedsupercomputingplatformfornextgenerationvirtualdrugdiscovery |