ArticleFrontiers in chemistry2024
Machine learning and molecular docking prediction of potential inhibitors against dengue virus.
Article in Frontiers in chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Amazonian Bioactives in Biopolymer-Based Systems for Biomedical Applications: Current Advances and Future Perspectives.Materials (Basel, Switzerland) · 2026Review
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- MARVpred: machine learning prediction of inhibitors targeting Marburg virus Gene 4 Small ORF protein.BMC infectious diseases · 2026Article
- Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats.Frontiers in pharmacology · 2026Review
- Machine Learning-Based QSAR Screening of Colombian Medicinal Flora for Potential Antiviral Compounds Against Dengue Virus: An In Silico Drug Discovery Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- A comparative evaluation of multiple machine learning approaches for forecasting dengue outbreaks in Bangladesh.Scientific reports · 2025Article
- RareInsight simplifies the communication of genetic results for rare disease patients.Scientific reports · 2025Article
- Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 2025Article
- TargetingFrontiers in bioinformatics · 2025Article
- Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, Method: Utilizing a dataset of 21,250 bioactive compounds from PubChem (AID: 651640), alongside a total of 1,444 descriptors generated using PaDEL, we trained various models such as Support Vector Machine, Random Forest, k-nearest neighbors, Logistic Regression, and Gaussian Naïve Bayes. The top-performing model was used to predict active compounds, followed by molecular docking performed using AutoDock Vina. The detailed interactions, toxicity, stability, and conformational changes of selected compounds were assessed through protein-ligand interaction studies, molecular dynamics (MD) simulations, and binding free energy calculations. Results: We implemented a robust three-dataset splitting strategy, employing the Logistic Regression algorithm, which achieved an accuracy of 94%. The model successfully predicted 18 known DENV inhibitors, with 11 identified as active, paving the way for further exploration of 2683 new compounds from the ZINC and EANPDB databases. Subsequent molecular docking studies were performed on the NS2B/NS3 protease, an enzyme essential in viral replication. ZINC95485940, ZINC38628344, 2',4'-dihydroxychalcone and ZINC14441502 demonstrated a high binding affinity of -8.1, -8.5, -8.6, and -8.0 kcal/mol, respectively, exhibiting stable interactions with His51, Ser135, Leu128, Pro132, Ser131, Tyr161, and Asp75 within the active site, which are critical residues involved in inhibition. Molecular dynamics simulations coupled with MMPBSA further elucidated the stability, making it a promising candidate for drug development. Conclusion: Overall, this integrative approach, combining machine learning, molecular docking, and dynamics simulations, highlights the strength and utility of computational tools in drug discovery. It suggests a promising pathway for the rapid identification and development of novel antiviral drugs against DENV. These
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