期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 20, 期 -, 页码 2831-2838出版社
ELSEVIER
DOI: 10.1016/j.csbj.2022.06.004
关键词
Protein-ligand interactions; Drug-target binding affinity; Drug discovery; Machine learning
资金
- National Natural Science Foundation of China [62171164, 62102191, 61872114, 62131004]
This review provides a brief introduction to computation-based protein-ligand interactions (PLIs) and discusses various approaches, with a particular focus on machine learning methods. It also analyzes three research dynamics that can be further explored in future studies.
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.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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