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 statisti-cally 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 q2=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.
Highlights
Malaria is one of the deadly diseases causes due to the infection of Plasmodium spp and becomes a major public health problem of the world
Dataset preparation: A set of eight 4-nerolidylcatechol derivatives with their diverse experimentally known inhibitory activity (IC50) data was compiled from the literature. 4-nerolidylcatechol is a semi synthetic derivative of catechol known for its antimalarial activity. (Bagatela et al, 2013; Pinto et al, 2009; Rocha et al, 2011; Lima et al, 2012)
We have employed the Poling algorithm to generate maximum of 255 diverse conformations with energy threshold of 20 kcal/mol above the calculated energy minimum for every training set compounds
Summary
Malaria is one of the deadly diseases causes due to the infection of Plasmodium spp and becomes a major public health problem of the world. A Partial Least-Squares (PLS) based 3D QSAR model on the 4-nerolidylcatechol derivatives was built and used for the identification of novel antimalarial compounds in the cheminformatics Prediction of physiochemical proper -ties of the dataset compounds such as hydrogen bond donor, hydrogen bond acceptor, Alogp, Molecular Weight (In Dalton), etc were computed for drug likeness study (Gogoi et al, 2014).
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