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Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to pre...

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Detalles Bibliográficos
Autores principales: Dhakal, Ashwin, McKay, Cole, Tanner, John J, Cheng, Jianlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8690157/
https://www.ncbi.nlm.nih.gov/pubmed/34849575
http://dx.doi.org/10.1093/bib/bbab476
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author Dhakal, Ashwin
McKay, Cole
Tanner, John J
Cheng, Jianlin
author_facet Dhakal, Ashwin
McKay, Cole
Tanner, John J
Cheng, Jianlin
author_sort Dhakal, Ashwin
collection PubMed
description New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions.
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spelling pubmed-86901572022-01-05 Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions Dhakal, Ashwin McKay, Cole Tanner, John J Cheng, Jianlin Brief Bioinform Review New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions. Oxford University Press 2021-11-27 /pmc/articles/PMC8690157/ /pubmed/34849575 http://dx.doi.org/10.1093/bib/bbab476 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review
Dhakal, Ashwin
McKay, Cole
Tanner, John J
Cheng, Jianlin
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title_full Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title_fullStr Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title_full_unstemmed Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title_short Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
title_sort artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8690157/
https://www.ncbi.nlm.nih.gov/pubmed/34849575
http://dx.doi.org/10.1093/bib/bbab476
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