AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the developm...

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Vydané v:	International journal of molecular sciences Ročník 24; číslo 9; s. 8083
Hlavní autori:	Andrianov, Alexander M., Shuldau, Mikita A., Furs, Konstantin V., Yushkevich, Artsemi M., Tuzikov, Alexander V.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 29.04.2023 MDPI
Predmet:	Analysis Artificial Intelligence Clinical trials Coronavirus 3C Proteases - antagonists & inhibitors Coronaviruses COVID-19 COVID-19 Drug Treatment Deep learning Drug Discovery - methods Drugs FDA approval Health aspects Ligands Machine learning Models, Molecular Neural networks Neural Networks, Computer Pharmacokinetics Proteases Proteins Severe acute respiratory syndrome coronavirus 2 Small Molecule Libraries - pharmacology Small Molecule Libraries - therapeutic use Viral proteins deep learning generative autoencoder SARS-CoV-2 molecular docking virtual screening main protease binding free energy calculations anti-SARS-CoV-2 drugs molecular dynamics
ISSN:	1422-0067, 1661-6596, 1422-0067
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Popis
Shrnutí:	Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1422-0067 1661-6596 1422-0067
DOI:	10.3390/ijms24098083