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Improving structural similarity based virtual screening using background knowledge
BACKGROUND: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3928642/ https://www.ncbi.nlm.nih.gov/pubmed/24341870 http://dx.doi.org/10.1186/1758-2946-5-50 |
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author | Girschick, Tobias Puchbauer, Lucia Kramer, Stefan |
author_facet | Girschick, Tobias Puchbauer, Lucia Kramer, Stefan |
author_sort | Girschick, Tobias |
collection | PubMed |
description | BACKGROUND: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods. RESULTS: In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods. CONCLUSION: Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds. |
format | Online Article Text |
id | pubmed-3928642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-39286422014-03-05 Improving structural similarity based virtual screening using background knowledge Girschick, Tobias Puchbauer, Lucia Kramer, Stefan J Cheminform Research Article BACKGROUND: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods. RESULTS: In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods. CONCLUSION: Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds. BioMed Central 2013-12-16 /pmc/articles/PMC3928642/ /pubmed/24341870 http://dx.doi.org/10.1186/1758-2946-5-50 Text en Copyright © 2013 Girschick et al.; licensee Chemistry Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Girschick, Tobias Puchbauer, Lucia Kramer, Stefan Improving structural similarity based virtual screening using background knowledge |
title | Improving structural similarity based virtual screening using background knowledge |
title_full | Improving structural similarity based virtual screening using background knowledge |
title_fullStr | Improving structural similarity based virtual screening using background knowledge |
title_full_unstemmed | Improving structural similarity based virtual screening using background knowledge |
title_short | Improving structural similarity based virtual screening using background knowledge |
title_sort | improving structural similarity based virtual screening using background knowledge |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3928642/ https://www.ncbi.nlm.nih.gov/pubmed/24341870 http://dx.doi.org/10.1186/1758-2946-5-50 |
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