Open AccessDesign of SARS-CoV-2 Mpro, PLpro dual-target inhibitors based on deep reinforcement learning and virtual screening
Author(s) -
L. Zhang,
Huilin Zhao,
Jin Liu,
Lei He,
Rilei Yu,
ChangHee Kang
Publication year - 2022
Publication title -
future medicinal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.708
H-Index - 69
eISSN - 1756-8927
pISSN - 1756-8919
DOI - 10.4155/fmc-2021-0269
Subject(s) - protease , virtual screening , papain , docking (animal) , chemistry , computational biology , covid-19 , biochemistry , amino acid , viral replication , biology , virology , virus , drug discovery , enzyme , medicine , nursing , disease , pathology , infectious disease (medical specialty)
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 & 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.