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

Background: Malaria remains a significant and worldwide health threat with increasing resistance to current treatments, stimulating the demand for innovative approaches in pursuing drug discovery. This systematic review integrates the progress made from 2014 through 2024 regarding molecular methods like gene expression profiling, molecular docking and machine learning to understand the biology of Methodology: Several studies were found using a PRISMA-guided search of PubMed, Scopus and Web of Science (64 studies found). The data extracted were gene expression outcomes, docking affinities, ML models and experimental validations (in vitro/in vivo). Results: Molecular docking emerged as the dominant technique (32.37%), followed by in vitro antiplasmodial assays (14.39%), ADMET profiling (10.79%) and gene expression studies (3.60%). RNA-seq analysis revealed key host and parasite genes modulated by herbal treatments, including those involved in apoptosis and inflammation. Notably, compounds like isorhamnetin and myricetin 3-O-glucoside showed exceptionally high binding affinities to Conclusion: Multiomics, docking and ML integration improve the target identification and prioritise the compounds. This review illustrates the great potential of molecular techniques for the development of drugs against antimalarial helicases that are not resistant to drug therapy. However, in vivo data holes and methodology inconsistency limit clinical translation. Future work should include standardisation of protocols and studies of synergistic combinations of phytochemicals.