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Related Experiment Video

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Benchmarking the Structure-Based Virtual Screening Performance of Wild-Type and Resistant PfDHFR Using Docking and

Menna S Hany1, Nermin S Ahmed2, Frank M Boeckler3,4

  • 1Pharmaceutical Chemistry Department, Faculty of Biotechnology, German International University, Cairo, Egypt.

Drug Design, Development and Therapy
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

This study benchmarks docking tools for malaria drug discovery. Machine learning re-scoring significantly improves identifying drug candidates against resistant Plasmodium falciparum Dihydrofolate Reductase (PfDHFR).

Keywords:
DEKOIS 2.0MLSFsdockingmalaria

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Area of Science:

  • Computational chemistry and drug discovery
  • Parasitology and infectious diseases

Background:

  • Malaria, caused by Plasmodium falciparum, poses a significant global health threat.
  • The parasite's Dihydrofolate Reductase (PfDHFR) enzyme is a crucial drug target.
  • Mutations in PfDHFR confer resistance to antifolate antimalarials like pyrimethamine.

Purpose of the Study:

  • To benchmark structure-based virtual screening (SBVS) tools against wild-type (WT) and mutant (Q) PfDHFR.
  • To evaluate the impact of machine learning scoring functions (ML SFs) on SBVS performance.
  • To provide recommendations for enhancing SBVS in antimalarial drug discovery.

Main Methods:

  • Evaluated three docking tools (AutoDock Vina, PLANTS, FRED) using the DEKOIS 2.0 benchmark.
  • Assessed two ML SFs (CNN-Score, RF-Score-VS v2) for re-scoring docking outcomes.
  • Analyzed screening performance using pROC-AUC, pROC-Chemotype plots, and EF 1%.

Main Results:

  • PLANTS with CNN re-scoring yielded the best enrichment for WT PfDHFR (EF 1% = 28).
  • FRED with CNN re-scoring showed superior enrichment for the Q variant (EF 1% = 31).
  • ML re-scoring substantially improved AutoDock Vina's performance for both variants.
  • Re-scoring effectively retrieved diverse, high-affinity binders at early enrichment stages.

Conclusions:

  • CNN-Score consistently enhances SBVS performance for both WT and Q PfDHFR.
  • ML re-scoring improves the identification of diverse and high-affinity binders.
  • These findings support improved antimalarial drug discovery, particularly against resistant strains.