Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict D...

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Autores principales:	Luong Huu Dang, Nguyen Tan Dung, Ly Xuan Quang, Le Quang Hung, Ngoc Hoang Le, Nhi Thao Ngoc Le, Nguyen Thi Diem, Nguyen Thi Thuy Nga, Shih-Han Hung, Nguyen Quoc Khanh Le
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	drug-drug interaction histamine antagonist machine learning PyBioMed package cheminformatics SMILES Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/889f42ba036d454b8b4c79b71c362bcb
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spelling	oai:doaj.org-article:889f42ba036d454b8b4c79b71c362bcb2021-11-25T17:11:24ZMachine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features10.3390/cells101130922073-4409https://doaj.org/article/889f42ba036d454b8b4c79b71c362bcb2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4409/10/11/3092https://doaj.org/toc/2073-4409The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.Luong Huu DangNguyen Tan DungLy Xuan QuangLe Quang HungNgoc Hoang LeNhi Thao Ngoc LeNguyen Thi DiemNguyen Thi Thuy NgaShih-Han HungNguyen Quoc Khanh LeMDPI AGarticledrug-drug interactionhistamine antagonistmachine learningPyBioMed packagecheminformaticsSMILESBiology (General)QH301-705.5ENCells, Vol 10, Iss 3092, p 3092 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drug-drug interaction histamine antagonist machine learning PyBioMed package cheminformatics SMILES Biology (General) QH301-705.5
spellingShingle	drug-drug interaction histamine antagonist machine learning PyBioMed package cheminformatics SMILES Biology (General) QH301-705.5 Luong Huu Dang Nguyen Tan Dung Ly Xuan Quang Le Quang Hung Ngoc Hoang Le Nhi Thao Ngoc Le Nguyen Thi Diem Nguyen Thi Thuy Nga Shih-Han Hung Nguyen Quoc Khanh Le Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
description	The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.
format	article
author	Luong Huu Dang Nguyen Tan Dung Ly Xuan Quang Le Quang Hung Ngoc Hoang Le Nhi Thao Ngoc Le Nguyen Thi Diem Nguyen Thi Thuy Nga Shih-Han Hung Nguyen Quoc Khanh Le
author_facet	Luong Huu Dang Nguyen Tan Dung Ly Xuan Quang Le Quang Hung Ngoc Hoang Le Nhi Thao Ngoc Le Nguyen Thi Diem Nguyen Thi Thuy Nga Shih-Han Hung Nguyen Quoc Khanh Le
author_sort	Luong Huu Dang
title	Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
title_short	Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
title_full	Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
title_fullStr	Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
title_full_unstemmed	Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features
title_sort	machine learning-based prediction of drug-drug interactions for histamine antagonist using hybrid chemical features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/889f42ba036d454b8b4c79b71c362bcb
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Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features

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