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

• Main Authors: Andrianov, A.M (Author), Furs, K.V (Author), Shuldau, M.A (Author), Tuzikov, A.V (Author), Yushkevich, A.M (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: anti-SARS-CoV-2 drugs; binding free energy calculations; deep learning; generative autoencoder; main protease; molecular docking; molecular dynamics; SARS-CoV-2; virtual screening
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023 by the authors.