DrugDevCovid19: An Atlas of Anti-COVID-19 Compounds Derived by Computer-Aided Drug Design

被引:10
|
作者
Liu, Yang [1 ]
Gan, Jianhong [1 ]
Wang, Rongqi [1 ]
Yang, Xiaocong [1 ]
Xiao, Zhixiong [1 ]
Cao, Yang [1 ,2 ]
机构
[1] Sichuan Univ, Minist Educ, Key Lab Bioresource & Ecoenvironm, Ctr Growth Metab & Aging, Chengdu 610000, Peoples R China
[2] Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02110 USA
来源
MOLECULES | 2022年 / 27卷 / 03期
基金
中国国家自然科学基金;
关键词
COVID-19; SARS-CoV-2; computer-aided drug design; bioinformatics; database; DE-NOVO DESIGN; MAIN PROTEASE; INHIBITORS; DISCOVERY; DOCKING; SARS-COV-2; ASSOCIATION; SIMULATION; STRATEGIES; BINDING;
D O I
10.3390/molecules27030683
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.
引用
收藏
页数:17
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