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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...

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Autores principales: Girschick, Tobias, Puchbauer, Lucia, Kramer, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
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.
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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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