Abstract:
Background: Malaria, caused by Plasmodium falciparum, is the primary cause of severe
malaria and poses a global health challenge. Despite prevention and control strategies, the
increase in disease burden, drug resistance, life and economic loss necessitates novel therapeutic
strategies. In this study, we employed computer-aided drug discovery (CADD) to identify
inhibitors targeting two vital P. falciparum proteins, lipoate protein ligase-2 (PfLipL2) and
lysophospholipase (PfLPL3), using AlphaFold-predicted structures.
Objectives: The general objective of the study was to leverage computational methods (virtual
screening, docking, consensus ranking and residue interaction analysis) to identify and validate
candidate antimalarial compounds targeting vital P. falciparum proteins from January to 2024 to
June 2025.
Methods and Materials: An AlphaFold‟s built in and Ramachandran plot was used for the 3D
model‟s structure quality validation. Virtual screening was carried out with MTiOpenScreen
server against protein targets and ADME properties were analyzed by SwissADME. Docking
was carried out using SwissDock and PyRx and ECR for prioritized candidates. Finally,
Discovery studio was used for protein-ligand‟s pose analysis.
Results: The 3D models had high structure quality. We screened 14,346 ligands, assessed
ADME properties, and docked 68 Lipinski-compliant ligands. Top three (10%) of prioritized
ligands ZINC000000537750, ZINC000001846243 and ZINC000030691430 for PfLipL2, and
ZINC000043203317, ZINC000001481805 and ZINC000004098812 for PfLPL3 were identified.
We found one top-ranked ligand with confirmed antimalarial activity against each target in
PubChem.
Conclusions and Recommendation: The novelty of this work stems from its methodological
synergies: dual-tool docking for enhanced reliability and ECR-augmented prioritization for
superior ranking fostering greater confidence in hit selection. These results nominate promising
candidates for future experimental validation toward new antimalarial.
