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Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the lin...

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Autores principales: Pedretti, Alessandro, Mazzolari, Angelica, Gervasoni, Silvia, Vistoli, Giulio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540224/
https://www.ncbi.nlm.nih.gov/pubmed/31027337
http://dx.doi.org/10.3390/ijms20092060
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author Pedretti, Alessandro
Mazzolari, Angelica
Gervasoni, Silvia
Vistoli, Giulio
author_facet Pedretti, Alessandro
Mazzolari, Angelica
Gervasoni, Silvia
Vistoli, Giulio
author_sort Pedretti, Alessandro
collection PubMed
description The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.
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spelling pubmed-65402242019-06-04 Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns Pedretti, Alessandro Mazzolari, Angelica Gervasoni, Silvia Vistoli, Giulio Int J Mol Sci Article The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average. MDPI 2019-04-26 /pmc/articles/PMC6540224/ /pubmed/31027337 http://dx.doi.org/10.3390/ijms20092060 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pedretti, Alessandro
Mazzolari, Angelica
Gervasoni, Silvia
Vistoli, Giulio
Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title_full Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title_fullStr Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title_full_unstemmed Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title_short Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns
title_sort rescoring and linearly combining: a highly effective consensus strategy for virtual screening campaigns
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540224/
https://www.ncbi.nlm.nih.gov/pubmed/31027337
http://dx.doi.org/10.3390/ijms20092060
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