A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19
Authors
Chattarin Ruchawapol
aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
Wen-Wei Fu
aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
Hong-Xi Xu
aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
Keywords:
Computational approaches, Traditional Chinese Medicine (TCM), Structure-based approach, Knowledge-mining, Network-based approach
Abstract
COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.
Purpose
This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.
Methods
A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.
Results
In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.
Conclusion
Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.
Keywords: Computational approaches, Traditional Chinese Medicine (TCM), Structure-based approach, Knowledge-mining, Network-based approach
Abbreviations: TCM, Traditional Chinese medicine; CADD, Computer-aided drug design; AI, Artificial Intelligence; MD, Molecular dynamics; MOA, Mechanism of action; ADMET, Absorption, distribution, metabolism, excretion, and toxicity; QSAR, Quantitative structure–activity relationship; HTVS, High-Throughput virtual screening; SBDD, Structure-based drug discovery; MM/PBSA, Molecular Mechanics Poisson-Boltzmann Surface area; MM/GBSA, Molecular Mechanics Generalized Born Surface area; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-protein interaction; MXSGD, Maxing Shigan Decoction; LHQWC, Lianhua Qingwen Capsule; QFPDT, Qingfei Paidu Tang; SPR, Surface plasmon resonance; GTM, Generative Topographic Mapping; DL, Deep learning; KG, Knowledge graph; ML, Machine Learning; OB, Oral bioavailability; CNN, Convolutional neural network; NCEs, New chemical entities
Author Biographies
Chattarin Ruchawapol, aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
bEngineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China
Wen-Wei Fu, aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
bEngineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China
Hong-Xi Xu, aSchool of Pharmacy, Shanghai University of Traditional Chinese Medicine, Cai Lun Lu 1200, Shanghai 201203, China
bEngineering Research Centre of Shanghai Colleges for TCM New Drug Discovery, Cai Lun Lu 1200, Shanghai 201203, China
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