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

• Published in: International journal of molecular sciences 2023-04, Vol.24 (9), p.8083
• Main Authors: Andrianov, Alexander M, Shuldau, Mikita A, Furs, Konstantin V, Yushkevich, Artsemi M, Tuzikov, Alexander V
• Format: Article
• Language: English
• Subjects: Analysis; Artificial Intelligence; Catalytic activity; Clinical trials; Coronavirus 3C Proteases - antagonists & inhibitors; Coronaviruses; COVID-19; COVID-19 Drug Treatment; Deep learning; Drug development; Drug Discovery - methods; Drugs; FDA approval; Free energy; generative autoencoder; Health aspects; Inhibitors; Ligands; Machine learning; main protease; Modelling; Models, Molecular; molecular docking; Molecular modelling; Neural networks; Neural Networks, Computer; Pharmacokinetics; Pharmacology; Protease; Proteases; Proteins; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Small Molecule Libraries - pharmacology; Small Molecule Libraries - therapeutic use; Therapeutic targets; Viral proteins; virtual screening
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24098083

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.