Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately bui...

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Autores principales:	Kun-Yi Hsin, Samik Ghosh, Hiroaki Kitano
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4f097faf2dc64a90b8ac8a8ebc857a98
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spelling	oai:doaj.org-article:4f097faf2dc64a90b8ac8a8ebc857a982021-11-18T08:39:29ZCombining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.1932-620310.1371/journal.pone.0083922https://doaj.org/article/4f097faf2dc64a90b8ac8a8ebc857a982013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24391846/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.Kun-Yi HsinSamik GhoshHiroaki KitanoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e83922 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kun-Yi Hsin Samik Ghosh Hiroaki Kitano Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
description	Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate.
format	article
author	Kun-Yi Hsin Samik Ghosh Hiroaki Kitano
author_facet	Kun-Yi Hsin Samik Ghosh Hiroaki Kitano
author_sort	Kun-Yi Hsin
title	Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
title_short	Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
title_full	Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
title_fullStr	Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
title_full_unstemmed	Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
title_sort	combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/4f097faf2dc64a90b8ac8a8ebc857a98
work_keys_str_mv	AT kunyihsin combiningmachinelearningsystemsandmultipledockingsimulationpackagestoimprovedockingpredictionreliabilityfornetworkpharmacology AT samikghosh combiningmachinelearningsystemsandmultipledockingsimulationpackagestoimprovedockingpredictionreliabilityfornetworkpharmacology AT hiroakikitano combiningmachinelearningsystemsandmultipledockingsimulationpackagestoimprovedockingpredictionreliabilityfornetworkpharmacology
_version_	1718421499857076224

Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.

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