GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds

Abstract The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molec...

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Autores principales:	Marawan Ahmed, Horia Jalily Hasani, Subha Kalyaanamoorthy, Khaled Barakat
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da2917f1c99f40dfa02c9cff21fddc59
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spelling	oai:doaj.org-article:da2917f1c99f40dfa02c9cff21fddc592021-12-02T15:38:23ZGPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds10.1038/s41598-021-88939-52045-2322https://doaj.org/article/da2917f1c99f40dfa02c9cff21fddc592021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88939-5https://doaj.org/toc/2045-2322Abstract The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews’ correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.Marawan AhmedHoria Jalily HasaniSubha KalyaanamoorthyKhaled BarakatNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marawan Ahmed Horia Jalily Hasani Subha Kalyaanamoorthy Khaled Barakat GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
description	Abstract The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews’ correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.
format	article
author	Marawan Ahmed Horia Jalily Hasani Subha Kalyaanamoorthy Khaled Barakat
author_facet	Marawan Ahmed Horia Jalily Hasani Subha Kalyaanamoorthy Khaled Barakat
author_sort	Marawan Ahmed
title	GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
title_short	GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
title_full	GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
title_fullStr	GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
title_full_unstemmed	GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
title_sort	gpcr_ligandclassify.py; a rigorous machine learning classifier for gpcr targeting compounds
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da2917f1c99f40dfa02c9cff21fddc59
work_keys_str_mv	AT marawanahmed gpcrligandclassifypyarigorousmachinelearningclassifierforgpcrtargetingcompounds AT horiajalilyhasani gpcrligandclassifypyarigorousmachinelearningclassifierforgpcrtargetingcompounds AT subhakalyaanamoorthy gpcrligandclassifypyarigorousmachinelearningclassifierforgpcrtargetingcompounds AT khaledbarakat gpcrligandclassifypyarigorousmachinelearningclassifierforgpcrtargetingcompounds
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GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds

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