3D QSAR modeling of 4-nerolidylcatechol derivatives and virtual screening for identification of potent <i>plasmodium</i> inhibitor

Abstract

The present study was aim to develop a three dimensional quantitative structure-activity relationships (3D QSAR) model based on the structure of 4-nerolidylcatechol (IC50=0.67 µM), a novel plant derived Plasmodium inhibitor and its derivatives for identification of efficient antimalarial lead. A statistically validated Partial Least-Squares (PLS) based Molecular Field Analysis (MFA) model was built up using the training set of eight 4-nerolidylcatechol derivatives and their diverse conformers. A statistically reliable model with good predictive power (cross-validated correlation coefficient q 2 =0.769) was obtained. Hence, the generated model was used to screen a library of 30,000 compounds of chembridge database (http://www.chembridge.com). Results of drug likeness prediction and ADMET study has suggested six compounds as potential antimalarial/plasmodial lead. Article Info