Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery.

Autor: Chuntakaruk H; Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.; Department of Biochemistry, Faculty of Science, Center of Excellence in Structural and Computational Biology, Chulalongkorn University, Bangkok, Thailand., Boonpalit K; School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand., Kinchagawat J; School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand., Nakarin F; School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand., Khotavivattana T; Center of Excellence in Natural Products Chemistry (CENP), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand., Aonbangkhen C; Center of Excellence in Natural Products Chemistry (CENP), Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand., Shigeta Y; Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan., Hengphasatporn K; Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan., Nutanong S; School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand., Rungrotmongkol T; Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.; Department of Biochemistry, Faculty of Science, Center of Excellence in Structural and Computational Biology, Chulalongkorn University, Bangkok, Thailand., Hannongbua S; Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.; Department of Chemistry, Faculty of Science, Center of Excellence in Computational Chemistry (CECC), Chulalongkorn University, Bangkok, Thailand.
Jazyk: angličtina
Zdroj: Journal of computational chemistry [J Comput Chem] 2024 May 15; Vol. 45 (13), pp. 953-968. Date of Electronic Publication: 2024 Jan 04.
DOI: 10.1002/jcc.27298
Abstrakt: In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy ( ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.
(© 2024 Wiley Periodicals LLC.)
Databáze: MEDLINE
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