research-article

Drug-target continuous binding affinity prediction using multiple sources of information

Authors: Betsabeh Tanoori, Mansoor Zolghadri Jahromi, Eghbal G. MansooriAuthors Info & Claims

Volume 186, Issue C

https://doi.org/10.1016/j.eswa.2021.115810

Published: 30 December 2021 Publication History

Highlights

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Integrating various sources of information to predict drug-target binding affinity.

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Takes advantages of both similarities and features.

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Gradient boosting regression model performs well in predicting new drug and new.

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Using various sources boost the accuracy of drug-target identification.

Abstract

Drug-target binding affinity prediction has a significant role in the search for new drugs or novel targets for existing drugs. The vast majority of recent computational approaches, presented for the task of drug-target binding affinity prediction, make use of a single source to measure drug-drug or protein-protein similarities. Incorporating various information sources is of the essence for improving the accuracy of drug-target prediction. The main objective of this research is to propose a method for combining the information provided from various similarity measures for drug-drug and protein-protein similarities and to show that this leads to better prediction performance. For this purpose, we propose a method that makes use of five drug-drug and five protein-protein similarity measures simultaneously to predict the binding affinity value of an input query drug-protein interaction. In the proposed method, using each pair of drug-drug and protein-protein similarity measures, k-nearest neighbor algorithm is used to find k drug-protein pairs most similar to the input interaction. The information regarding the binding affinity values of neighbors and their similarities are fed as features to a gradient boosting machine to construct the regression model. To assess the performance of our method in comparison with state-of-the-art methods in the literature, three related benchmark datasets were used. The experimental results in various settings (pairwise, new drug, and new target scenarios) indicate the superiority of the proposed method in comparison with other methods proposed in the literature.

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Cited By

Pei QWu LHe ZZhu JXia YXie SYan RSerra ESpezzano F(2024)Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest NeighborsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679704(1856-1866)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679704
Chen GHe HZhao LLv QChen C(2024)GINCM-DTAExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121274236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121274
Dehghan ARazzaghi PAbbasi KGharaghani S(2023)TripletMultiDTIExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120754232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120754
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Drug-target continuous binding affinity prediction using multiple sources of information

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 186, Issue C

Dec 2021

1489 pages

ISSN:0957-4174

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 30 December 2021

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Cited By

Pei QWu LHe ZZhu JXia YXie SYan RSerra ESpezzano F(2024)Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest NeighborsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679704(1856-1866)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679704
Chen GHe HZhao LLv QChen C(2024)GINCM-DTAExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121274236:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121274
Dehghan ARazzaghi PAbbasi KGharaghani S(2023)TripletMultiDTIExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120754232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120754
Chakraborty RHasija Y(2023)Utilizing deep learning to explore chemical space for drug lead optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120592229:PAOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120592
Gupta PMajumdar AChouzenoux EChierchia G(2023)DeConDFFuse Expert Systems with Applications: An International Journal10.1016/j.eswa.2023.120238227:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120238

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