Design of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening

被引:24
|
作者
Zhang, Li-chuan [1 ]
Zhao, Hui-lin [1 ]
Liu, Jin [1 ]
He, Lei [1 ]
Yu, Ri-lei [2 ]
Kang, Cong-min [1 ]
机构
[1] Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, Peoples R China
[2] Ocean Univ China, Sch Med & Pharm, Key Lab Marine Drugs, Chinese Minist Educ, Qingdao 266003, Peoples R China
基金
中国国家自然科学基金;
关键词
covalent docking; deep reinforcement learning; drug design; molecular dynamics simulation; Mpro; PLpro; SARS-CORONAVIRUS; COVALENT INHIBITORS; DRUG DISCOVERY;
D O I
10.4155/fmc-2021-0269
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Background: Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the inhibition of SARS-CoV-2 virus replication and proliferation. Materials & methods: In this study, deep reinforcement learning, covalent docking and molecular dynamics simulations were used to identify novel compounds that have the potential to inhibit both Mpro and PLpro. Results and conclusion: Three compounds were identified that can effectively occupy the Mpro protein cavity with the PLpro protein cavity and form high frequency contacts with key amino acid residues (Mpro: His41, Cys145, Glu166, PLpro: Cys111). These three compounds can be further investigated as potential lead compounds for SARS-CoV-2 inhibitors.
引用
收藏
页码:393 / 405
页数:13
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