Machine Learning Model and Molecular Docking for Screening Medicinal Plants as HIV-1 Reverse Transcriptase Inhibitors

Authors

Muthia Rahayu Iresha, Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
Firdayani Firdayani, Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, IndonesiaFollow
Agam Wira Sani, Research Center for Sustainable Production System and Life Cycle Assessment, Research Organization for Energy and Manufacture, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
Nihayatul Karimah, Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesi
Shelvi Listiana, Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
Irfansyah Yudhi Tanasa, Research Center for Electronic, Research Organization for Electronic and Informatic, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
Arief Sartono, Research Center for Artificial Intelligence and Cybersecurity, Research Organization for Electronics and Informatic, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia
Ayu Masyita, Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia

Abstract

The human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) plays a significant role in viral replication and is one of the targets for anti-HIV. However, a mutation in viral strains rapidly developed the resistance of the com-pounds to the protein, reducing the effectiveness of the inhibitors. This work seeks to utilize machine learning-based quantitative structure-activity relationship (QSAR) analysis in combination with molecular docking simulations to forecast the presence of active compounds derived from medicinal plants. Specifically, the objective is to identify com-pounds that have the potential to operate as inhibitors of HIV-1 reverse transcriptase (RT), encompassing both wild-type and mutant variants. It is demonstrated that some substances are no longer suitable as inhibitors due to changes in the HIV-1 RT enzyme. Based on the screening results, four medicinal plants, Melissa officinalis, Punica granatum, Psidium guajava, and Curcuma longa, are worth further investigation. Nevertheless, the findings from the in vitro study suggest that extracts derived from pomegranate rind and guava leaves exhibit significant promise as HIV-1 RT inhibitors.

Recommended Citation

Iresha, Muthia Rahayu; Firdayani, Firdayani; Sani, Agam Wira; Karimah, Nihayatul; Listiana, Shelvi; Tanasa, Irfansyah Yudhi; Sartono, Arief; and Masyita, Ayu (2024) "Machine Learning Model and Molecular Docking for Screening Medicinal Plants as HIV-1 Reverse Transcriptase Inhibitors," Karbala International Journal of Modern Science: Vol. 10 : Iss. 1 , Article 7.
Available at: https://doi.org/10.33640/2405-609X.3341

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