Evidence map›Paper›PMID 39776767›Full record

ArticleFrontiers in chemistry2024

Machine learning and molecular docking prediction of potential inhibitors against dengue virus.

George Hanson, Joseph Adams, Daveson I B Kepgang, Luke S Zondagh, Lewis Tem Bueh, Andy Asante, Soham A Shirolkar, Maureen Kisaakye, Hem Bondarwad, Olaitan I Awe

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. TargetingFrontiers in bioinformatics · 2025
    Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

George HansonDepartment of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Accra, Ghana.
Joseph AdamsDepartment of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Accra, Ghana.
Daveson I B KepgangDepartment of Biochemistry, Faculty of Sciences, University of Douala, Douala, Cameroon.
Luke S ZondaghPharmaceutical Chemistry, School of Pharmacy, University of Western Cape Town, Cape Town, South Africa.
Lewis Tem BuehDepartment of Computer Engineering, Faculty of Engineering and Technology, University of Buea, Buea, Cameroon.
Andy AsanteDepartment of Immunology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Accra, Ghana.
Soham A ShirolkarCollege of Engineering, University of South Florida, Florida, United States.
Maureen KisaakyeDepartment of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda.
Hem BondarwadDepartment of Biotechnology and Bioinformatics, Deogiri College, Dr. Babasaheb Ambedkar Marathwada University, Sambhajinagar, India.
Olaitan I AweAfrican Society for Bioinformatics and Computational Biology, Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

dengue virusdrug discoverymachine learningmolecular dockingmolecular dynamics simulation

Identifiers

PMID39776767
PMCPMC11703810

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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.