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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

Updated: Mar 3, 2026

Methods to Investigate the Regulatory Role of Small RNAs and Ribosomal Occupancy of Plasmodium falciparum
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Gene Expression, Docking and Machine Learning in Malaria Drug Discovery: A Systematic Review.

Reuben Samson Dangana1, Israel Ehizuelen Ebhohimen2, Samson Anjikwi Malgwi1

  • 1Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal (Westville), Durban, South Africa.

Biochemistry Research International
|March 2, 2026
PubMed
Summary

Molecular docking and machine learning accelerate malaria drug discovery by identifying potent herbal compounds. Integrating multiomics data enhances target identification for novel antimalarial therapies against resistant Plasmodium strains.

Keywords:
Antimalarial drug discoveryPlasmodium speciesgene expression profilingmachine learningmolecular docking

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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.