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AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification

In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction. Our proposed model is inspired by sentence classificatio...

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Autores principales: Yazdani-Jahromi, Mehdi, Yousefi, Niloofar, Tayebi, Aida, Kolanthai, Elayaraja, Neal, Craig J, Seal, Sudipta, Garibay, Ozlem Ozmen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294423/
https://www.ncbi.nlm.nih.gov/pubmed/35817396
http://dx.doi.org/10.1093/bib/bbac272
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author Yazdani-Jahromi, Mehdi
Yousefi, Niloofar
Tayebi, Aida
Kolanthai, Elayaraja
Neal, Craig J
Seal, Sudipta
Garibay, Ozlem Ozmen
author_facet Yazdani-Jahromi, Mehdi
Yousefi, Niloofar
Tayebi, Aida
Kolanthai, Elayaraja
Neal, Craig J
Seal, Sudipta
Garibay, Ozlem Ozmen
author_sort Yazdani-Jahromi, Mehdi
collection PubMed
description In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug–target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug–target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug–target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications.
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spelling pubmed-92944232022-07-20 AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification Yazdani-Jahromi, Mehdi Yousefi, Niloofar Tayebi, Aida Kolanthai, Elayaraja Neal, Craig J Seal, Sudipta Garibay, Ozlem Ozmen Brief Bioinform Problem Solving Protocol In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction. Our proposed model is inspired by sentence classification models in the field of Natural Language Processing, where the drug–target complex is treated as a sentence with relational meaning between its biochemical entities a.k.a. protein pockets and drug molecule. AttentionSiteDTI enables interpretability by identifying the protein binding sites that contribute the most toward the drug–target interaction. Results on three benchmark datasets show improved performance compared with the current state-of-the-art models. More significantly, unlike previous studies, our model shows superior performance, when tested on new proteins (i.e. high generalizability). Through multidisciplinary collaboration, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict the binding interactions between some candidate compounds and a target protein, then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally predicted and experimentally observed (measured) drug–target interactions illustrates the potential of our method as an effective pre-screening tool in drug repurposing applications. Oxford University Press 2022-07-12 /pmc/articles/PMC9294423/ /pubmed/35817396 http://dx.doi.org/10.1093/bib/bbac272 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Problem Solving Protocol
Yazdani-Jahromi, Mehdi
Yousefi, Niloofar
Tayebi, Aida
Kolanthai, Elayaraja
Neal, Craig J
Seal, Sudipta
Garibay, Ozlem Ozmen
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title_full AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title_fullStr AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title_full_unstemmed AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title_short AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification
title_sort attentionsitedti: an interpretable graph-based model for drug-target interaction prediction using nlp sentence-level relation classification
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9294423/
https://www.ncbi.nlm.nih.gov/pubmed/35817396
http://dx.doi.org/10.1093/bib/bbac272
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