ArticleInternational journal of molecular sciences2025
Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
7 citing papers in PubMed.
- High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.Gut microbes · 2026Article
- MERS-Mpro Predictor: A Machine Learning-Based Tool for Rapid Screening of Potential MERS-CoV Main Protease Inhibitors.International journal of molecular sciences · 2026Article
- An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Identification of natural TLR4 modulators through network pharmacology and molecular modeling in SARS-CoV-2Frontiers in chemistry · 2026Article
- From Micro to Marvel: Unleashing the Full Potential of Click Chemistry with Micromachine Integration.Micromachines · 2025Review
- Deep learning-guided discovery of selective JAK2-JH2 allosteric inhibitors: integration of MLP predictive modeling, BREED-based library design, and computational validation.Frontiers in chemistry · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the Random Forest (RF) algorithm exhibited the highest predictive performance, achieving an accuracy of 0.97, an ROC-AUC score of 0.98, and an F1-score of 0.98. Following model validation, we applied it to a dataset of 14,194 naturally occurring compounds from PubChem. The top-ranked compounds were subsequently subjected to molecular docking, which identified Perenniporide B, Phellifuropyranone A, and Terrestrol G as the most promising candidates, with binding energies of -9.17, -9.08, and -8.71 kcal/mol, respectively. These compounds formed strong interactions with key catalytic residues, suggesting significant inhibitory potential against the viral protease. Furthermore, molecular dynamics simulations confirmed their stability within the active site, reinforcing their viability as antiviral agents. This study demonstrates the effectiveness of integrating machine learning with molecular modeling to accelerate the discovery of therapeutic candidates against emerging viral threats.
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Registered trials
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