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Training a Scoring Function for the Alignment of Small Molecules
[Image: see text] A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hyd...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2946173/ https://www.ncbi.nlm.nih.gov/pubmed/20831240 http://dx.doi.org/10.1021/ci100227h |
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author | Chan, Shek Ling Labute, Paul |
author_facet | Chan, Shek Ling Labute, Paul |
author_sort | Chan, Shek Ling |
collection | PubMed |
description | [Image: see text] A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods. |
format | Text |
id | pubmed-2946173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-29461732010-09-27 Training a Scoring Function for the Alignment of Small Molecules Chan, Shek Ling Labute, Paul J Chem Inf Model [Image: see text] A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods. American Chemical Society 2010-09-10 2010-09-27 /pmc/articles/PMC2946173/ /pubmed/20831240 http://dx.doi.org/10.1021/ci100227h Text en Copyright © 2010 American Chemical Society http://pubs.acs.org This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org. |
spellingShingle | Chan, Shek Ling Labute, Paul Training a Scoring Function for the Alignment of Small Molecules |
title | Training a Scoring Function for the Alignment of Small Molecules |
title_full | Training a Scoring Function for the Alignment of Small Molecules |
title_fullStr | Training a Scoring Function for the Alignment of Small Molecules |
title_full_unstemmed | Training a Scoring Function for the Alignment of Small Molecules |
title_short | Training a Scoring Function for the Alignment of Small Molecules |
title_sort | training a scoring function for the alignment of small molecules |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2946173/ https://www.ncbi.nlm.nih.gov/pubmed/20831240 http://dx.doi.org/10.1021/ci100227h |
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