Comparative study between 3D-QSAR and Docking-Based Pharmacophore models for potent Plasomodium falciparum dihydroorotate dehydrogenase inhibitors

Abstract

Malaria, caused by infections of the human malaria parasites Plasmodium falciparum, is a global infectious parasitic disease. Each year, about three million people died from malaria and the majority of whom are pregnant women and young children. Recently, a number of research attempt to reduce malaria parasite resistance and the toxicity of anti-malarial drugs. Nowadays, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) was validated as a potent drug target to inhibit malarial activity by blocking pyrimidine biosynthesis. In this study, we employed 3D-QSAR Pharmacophore Generation and Docking-Based Pharmacophore Development to build the pharmacophore by using the collected 67 effective inhibitors against PfDHODH. 3D-QSAR Pharmacophore model, Hypo1, shows the high correlation coefficient (0.935), the lowest RMS deviation (2.15), the predicting accuracy of successful rates to training set (89.4%) and test set compounds (72.4%), respectively, revealing favorable predictive ability and is a reliable for further study. Additionally, Docking-Based Pharmacophore model, DBP-All255, exhibits comparable predictive capability to that of Hypo1, while DBP-Top1 shows poor statistical significance. This study reveals pharmacophore features of Hypo1, built by 3D-QSAR Pharmacophore Generation, are well-complementary to the functional residues in the active site of PfDHODH and is of great reliable for database screening.

Keywords: 3D-QSAR Pharmacophore Generation; Catalyst; Docking-Based Pharmacophore Development; PfDHODH inhibitor; Pharmacophore.