Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Parasitology

Background:

• Malaria remains a significant global health threat, necessitating the discovery of new antimalarial drugs.
• Existing antimalarial treatments face challenges due to drug resistance.
• Computational methods offer a powerful approach for accelerating drug discovery.

Purpose of the Study:

• To develop and validate quantitative structure-activity relationship (QSAR) models for predicting antimalarial activity against P. falciparum.
• To virtually screen large compound libraries to identify novel antimalarial drug candidates.
• To validate computationally identified hits through experimental testing.

Main Methods:

• Development of QSAR models using a dataset of 3133 compounds, with strategies to address data imbalance.
• Rigorous internal and external validation of the QSAR models.
• Virtual screening of the ChemBridge database using the validated QSAR models.
• Experimental validation of predicted active compounds and negative controls.

Main Results:

• QSAR models achieved high balanced accuracy (87-100%) in predicting antimalarial activity on external validation sets.
• Virtual screening identified 176 potential antimalarial compounds.
• Experimental validation confirmed 25 (14.2%) hits with antimalarial activity and low cytotoxicity.
• All 42 predicted inactive compounds were experimentally confirmed as inactive.
• Novel chemical scaffolds were identified among the confirmed active compounds.

Conclusions:

• Validated QSAR models are effective tools for identifying novel antimalarial agents.
• The identified novel scaffolds represent promising starting points for developing new antimalarial drugs.
• This study demonstrates the utility of computational approaches in accelerating the discovery of urgently needed antimalarial therapies.