COMPUTATIONAL MODELING OF ANTIMALARIAL 10-SUBSTITUTED DEOXOARTEMISININS

Abstract

Nineteen 10-substitued deoxoartemisinin derivatives and artemisinin with activity against D-6 strains of malarial falciparum designated as Sierra Leone are studied. We use molecular electrostatic potential maps in an attempt to identify key structural features of the artemisinins that are necessary for their activities and molecular docking to investigate the interaction with the molecular receptor (heme). Chemometric modeling: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-Nearest Neighbor (KNN), Soft Independent Modeling of Class Analogy (SIMCA) and Stepwise Discriminant Analysis (SDA) are employed to reduce dimensionality and investigate which subset of descriptors are responsible for the classification between more active (MA) and less active (LA) artemisinins. The PCA, HCA, KNN, SIMCA and SDA studies showed that the descriptors LUMO (Lowest Unoccupied Molecular Orbital) energy, DFeO1 (Distance between the O 1 atom from ligand and iron atom from heme), X1A (Average Connectivity Index Chi-1) and Mor15u (Molecular Representation of Structure Based on Electron Diffraction) code of signal 15, unweighted, are responsible for separating the artemisinins according to their degree of antimalarial activity. The prediction study was done with a new set of eight artemisinins by using the chemometric methods and five of them were predicted as active against D-6 strains of falciparum malaria. In order to verify if the key structural features that are necessary for their antimalarial activities were investigated for the interaction with the heme, we also carried out calculations of the molecular electrostatic potential (MEP) and molecular docking. MEP maps and molecular docking were analyzed for more active compounds of the prediction set.