A machine learning framework for predicting drug–drug interactions

Abstract Understanding drug–drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to eluci...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Suyu Mei, Kun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9d896a473c24f599f2340990a20b4ba
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b9d896a473c24f599f2340990a20b4ba
record_format	dspace
spelling	oai:doaj.org-article:b9d896a473c24f599f2340990a20b4ba2021-12-02T16:38:49ZA machine learning framework for predicting drug–drug interactions10.1038/s41598-021-97193-82045-2322https://doaj.org/article/b9d896a473c24f599f2340990a20b4ba2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97193-8https://doaj.org/toc/2045-2322Abstract Understanding drug–drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug–drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug–drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug–drug interactions. Furthermore, we define several statistical metrics in the context of human protein–protein interaction networks and signaling pathways to measure the interaction intensity, interaction efficacy and action range between two drugs. Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein–protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. The unravelled mechanisms could provide biological insights into potential adverse drug reactions of co-prescribed drugs.Suyu MeiKun ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Suyu Mei Kun Zhang A machine learning framework for predicting drug–drug interactions
description	Abstract Understanding drug–drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods, commonly integrating heterogeneous data to increase model performance, often suffer from a high model complexity, As such, how to elucidate the molecular mechanisms underlying drug–drug interactions while preserving rational biological interpretability is a challenging task in computational modeling for drug discovery. In this study, we attempt to investigate drug–drug interactions via the associations between genes that two drugs target. For this purpose, we propose a simple f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug–drug interactions. Furthermore, we define several statistical metrics in the context of human protein–protein interaction networks and signaling pathways to measure the interaction intensity, interaction efficacy and action range between two drugs. Large-scale empirical studies including both cross validation and independent test show that the proposed drug target profiles-based machine learning framework outperforms existing data integration-based methods. The proposed statistical metrics show that two drugs easily interact in the cases that they target common genes; or their target genes connect via short paths in protein–protein interaction networks; or their target genes are located at signaling pathways that have cross-talks. The unravelled mechanisms could provide biological insights into potential adverse drug reactions of co-prescribed drugs.
format	article
author	Suyu Mei Kun Zhang
author_facet	Suyu Mei Kun Zhang
author_sort	Suyu Mei
title	A machine learning framework for predicting drug–drug interactions
title_short	A machine learning framework for predicting drug–drug interactions
title_full	A machine learning framework for predicting drug–drug interactions
title_fullStr	A machine learning framework for predicting drug–drug interactions
title_full_unstemmed	A machine learning framework for predicting drug–drug interactions
title_sort	machine learning framework for predicting drug–drug interactions
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b9d896a473c24f599f2340990a20b4ba
work_keys_str_mv	AT suyumei amachinelearningframeworkforpredictingdrugdruginteractions AT kunzhang amachinelearningframeworkforpredictingdrugdruginteractions AT suyumei machinelearningframeworkforpredictingdrugdruginteractions AT kunzhang machinelearningframeworkforpredictingdrugdruginteractions
_version_	1718383597724893184

A machine learning framework for predicting drug–drug interactions

Ejemplares similares