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Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go...

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Autores principales: García, Jerónimo S., Puertas-Martín, Savíns, Redondo, Juana L., Moreno, Juan José, Ortigosa, Pilar M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842220/
https://www.ncbi.nlm.nih.gov/pubmed/36687309
http://dx.doi.org/10.1007/s11227-022-05014-0
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author García, Jerónimo S.
Puertas-Martín, Savíns
Redondo, Juana L.
Moreno, Juan José
Ortigosa, Pilar M.
author_facet García, Jerónimo S.
Puertas-Martín, Savíns
Redondo, Juana L.
Moreno, Juan José
Ortigosa, Pilar M.
author_sort García, Jerónimo S.
collection PubMed
description Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.
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spelling pubmed-98422202023-01-17 Improving drug discovery through parallelism García, Jerónimo S. Puertas-Martín, Savíns Redondo, Juana L. Moreno, Juan José Ortigosa, Pilar M. J Supercomput Article Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered. Springer US 2023-01-16 2023 /pmc/articles/PMC9842220/ /pubmed/36687309 http://dx.doi.org/10.1007/s11227-022-05014-0 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
García, Jerónimo S.
Puertas-Martín, Savíns
Redondo, Juana L.
Moreno, Juan José
Ortigosa, Pilar M.
Improving drug discovery through parallelism
title Improving drug discovery through parallelism
title_full Improving drug discovery through parallelism
title_fullStr Improving drug discovery through parallelism
title_full_unstemmed Improving drug discovery through parallelism
title_short Improving drug discovery through parallelism
title_sort improving drug discovery through parallelism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842220/
https://www.ncbi.nlm.nih.gov/pubmed/36687309
http://dx.doi.org/10.1007/s11227-022-05014-0
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