ArticleInternational journal of molecular sciences2023
AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Drugs against broad-spectrum of coronaviruses.Frontiers in immunology · 2026Review
- Advances in Fragment-based Drug Design: Lessons and Innovations from the Post-COVID Drug Discovery Landscape.Mini reviews in medicinal chemistry · 2026Review
- Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics.International journal of molecular sciences · 2025Article
- Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery.RSC medicinal chemistry · 2025Review
- Design of Ig-like binders targeting α-synuclein fibril for mitigating its pathological activities.Nature communications · 2025Article
- Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning.NPJ digital medicine · 2025Article
- Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning.Viruses · 2025Article
- DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.Journal of chemical information and modeling · 2025Article
- Antiviral Drug Discovery.International journal of molecular sciences · 2024Article
- The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research.Current microbiology · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.