Gene Expression, Docking and Machine Learning in Malaria Drug Discovery: A Systematic Review

Area of Science:

• * Pharmacology
• * Computational Biology
• * Medicinal Chemistry

Background:

• * Malaria poses a significant global health challenge due to increasing drug resistance.
• * Novel therapeutic strategies are urgently needed for malaria drug discovery.
• * This review focuses on molecular methods and herbal remedies for antimalarial drug development.

Purpose of the Study:

• * To systematically review molecular methods for malaria drug discovery from 2014-2024.
• * To identify new drug targets and compounds, particularly herbal remedies.
• * To explore the integration of computational approaches in understanding Plasmodium biology.

Main Methods:

• * Systematic literature search (PubMed, Scopus, Web of Science) following PRISMA guidelines.
• * Extraction of data on gene expression, molecular docking, machine learning models, and experimental validations.
• * Analysis of 64 selected studies.

Main Results:

• * Molecular docking was the most prevalent technique (32.37%), followed by in vitro assays (14.39%).
• * RNA-seq identified host/parasite genes modulated by herbal treatments (e.g., apoptosis, inflammation).
• * Promising compounds (isorhamnetin, myricetin 3-O-glucoside) showed high binding affinity to drug targets (Plasmepsin II, PfLDH).
• * Machine learning models achieved high predictive accuracy (AUC up to 0.87) for bioactivity.
• * Key targeted pathways include hemoglobin degradation and glycolysis.

Conclusions:

• * Integration of multiomics, docking, and machine learning enhances target identification and compound prioritization.
• * Molecular techniques show great potential for developing non-resistant antimalarial drugs.
• * Gaps in in vivo data and methodological inconsistencies hinder clinical translation.
• * Future research should focus on standardizing protocols and investigating synergistic phytochemical combinations.