ArticleMolecules (Basel, Switzerland)2022
Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- TaLiRAGen: target-aware ligand generation via retrieval-augmented large language models.Molecular diversity · 2026Article
- Chemical probes for enzyme imaging: challenges in design, synthesis and biomedical applications.Chemical science · 2025Review
- More Than Three Decades After Discovery of the Neuroprotective Effect of PACAP, What Is Still Preventing Its Clinical Use?Journal of molecular neuroscience : MN · 2025Review
- A novel, covalent broad-spectrum inhibitor targeting human coronavirus MNature communications · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.Journal of chemical information and modeling · 2024Article
- Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Recent Advances in Automated Structure-Based De Novo Drug Design.Journal of chemical information and modeling · 2024Review
- Recent advances in the exploration and discovery of SARS-CoV-2 inhibitory peptides from edible animal proteins.Frontiers in nutrition · 2024Review
- Introducing the Automated Ligand Searcher.Journal of chemical information and modeling · 2023Article
- Multi-and many-objective optimization: present and future inFrontiers in chemistry · 2023Review
- The Potential of Stilbene Compounds to Inhibit MCurrent issues in molecular biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.
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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.