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Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective
Jameel M Abduljalil1, Abdo A Elfiky2
1School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, New South Wales 2006, Australia.
The Journal of Physical Chemistry. B
|November 1, 2024
Summary
Researchers used machine learning to screen 32,000 potential dengue virus (DENV) protease inhibitors. Comp530 shows promise as a noncovalent inhibitor, with opportunities for further drug development.
Area of Science:
- Virology
- Drug Discovery
- Computational Chemistry
Background:
- Dengue virus (DENV) poses a significant global health threat due to its increasing prevalence and lack of approved antiviral treatments.
- The DENV NS3/NS2B serine protease is crucial for viral replication, making it a key target for antiviral drug development.
Purpose of the Study:
- To identify novel inhibitors of the DENV NS3/NS2B serine protease using machine learning and virtual screening.
- To evaluate the drug-like properties and binding potential of identified inhibitor candidates.
Main Methods:
- Structure-based virtual screening of 32,000 compounds using GNINA.
- Assessment of ADMET properties (absorption, distribution, metabolism, excretion, toxicity).
- Molecular dynamics simulations and MM/GBSA calculations to determine binding free energy.
Main Results:
- Machine learning models successfully screened a large library of potential protease inhibitors.
- Comp530 demonstrated binding potential to the DENV protease as a noncovalent inhibitor.
- Identified compounds offer sites for chemical modification to enhance selectivity and specificity.
Conclusions:
- Machine learning and computational methods can effectively identify potential antiviral drug candidates for DENV.
- Comp530 represents a promising lead compound for developing new dengue antiviral therapies.
- Further optimization of identified compounds may lead to effective covalent or noncovalent inhibitors.
