With the aim of researching new antimalarial drugs, a series of quinoline, isoquinoline and quinazoline derivatives were studied against the Plasmodium falciparum CQ-sensitive and MQ-resistant strain 3D7 protozoan parasite. DFT with B3LYP functional and 6-311G basis set was used to calculate quantum chemical descriptors for QSAR models. The molecular mechanics (MM2) method was used to calculate constitutional, physicochemical, and topological descriptors. By randomly dividing the dataset into training and test sets, we were able to construct reliable models using linear regression (MLR), nonlinear regression (MNLR) and artificial neural networks (ANN). The determination coefficient values indicate the predictive quality of the established models. The robustness and predictive power of the generated models were also confirmed via internal validation, external validation, the Y-Randomization test and the applicability domain. Furthermore, molecular docking studies were conducted to identify the key interactions between the studied molecules and the PfPMT receptor's active site.
The findings of this contribution study indicate that the antimalarial activity of these compounds against Plasmodium falciparum appears to be largely determined by four descriptors, i.e., Total Connectivity (Tcon), percentage of carbon (C (%)), density (D) and bond length between the two nitrogen atoms (Bond N-N).
On the basis of the reliable QSAR model and molecular docking results, several new antimalarial compounds have been designed. The selection of drug-candidates was performed according to drug-likeness and ADMET parameters.