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A brief review of protein–ligand interaction prediction
The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the dru...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189993/ https://www.ncbi.nlm.nih.gov/pubmed/35765652 http://dx.doi.org/10.1016/j.csbj.2022.06.004 |
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author | Zhao, Lingling Zhu, Yan Wang, Junjie Wen, Naifeng Wang, Chunyu Cheng, Liang |
author_facet | Zhao, Lingling Zhu, Yan Wang, Junjie Wen, Naifeng Wang, Chunyu Cheng, Liang |
author_sort | Zhao, Lingling |
collection | PubMed |
description | The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies. |
format | Online Article Text |
id | pubmed-9189993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-91899932022-06-27 A brief review of protein–ligand interaction prediction Zhao, Lingling Zhu, Yan Wang, Junjie Wen, Naifeng Wang, Chunyu Cheng, Liang Comput Struct Biotechnol J Mini Review The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies. Research Network of Computational and Structural Biotechnology 2022-06-03 /pmc/articles/PMC9189993/ /pubmed/35765652 http://dx.doi.org/10.1016/j.csbj.2022.06.004 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Mini Review Zhao, Lingling Zhu, Yan Wang, Junjie Wen, Naifeng Wang, Chunyu Cheng, Liang A brief review of protein–ligand interaction prediction |
title | A brief review of protein–ligand interaction prediction |
title_full | A brief review of protein–ligand interaction prediction |
title_fullStr | A brief review of protein–ligand interaction prediction |
title_full_unstemmed | A brief review of protein–ligand interaction prediction |
title_short | A brief review of protein–ligand interaction prediction |
title_sort | brief review of protein–ligand interaction prediction |
topic | Mini Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189993/ https://www.ncbi.nlm.nih.gov/pubmed/35765652 http://dx.doi.org/10.1016/j.csbj.2022.06.004 |
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