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In silico studies are essential techniques in the modern medicinal chemistry. QSAR modeling and molecular docking are important techniques in both drug discovery and development and have been successfully deployed in the field of medicinal chemistry for the discovery, design, and development of many drug candidates. These techniques were used in this work to come up with a model that relate 2,5-disubstituted furans with their antiplasmodium activities for the development of more active antimalarial drugs. Predictive and robust QSAR model was generated using Genetic Function Algorithm. The model was statistically validated to have internal and external squared correlation coefficient, R2 of 0.982 and 0.735 respectively; predictive squared correlation coefficient, R2pred of 0.599; adjusted squared correlation coefficient, Radj of 0.974; and leave-one-out cross-validation coefficient, Q2cv of 0.966. It was found out that the antiplasmodium activities of 2,5-disubstituted furans relied on the parameters: GATS5c, minsCl RDF130m, RDF75p, and RDF115s descriptors. All the descriptors except minsCl influenced the antiplasmodium activities of the compounds negatively. That is, their increase decreases the activities of the furans and vice versa. The docking study revealed that most of the furans bind more tightly to Plasmodium falciparum lactate dehydrogenase (pfLDH) than chloroquine, but the enzyme may not be their major target. Insight into the relationship between 2,5-disubstituted furans and their antiplasmodium activities has been revealed from the results of this work. Therefore, this could serve as a model for designing novel 2,5-disubstituted furans as potential antimalarial drugs with better activities.

In silico studies of 2,5-disubstituted furans as active antimalarial drug candidates