First report on exploring classification and regression based QSAR modelling of Plasmodium falciparum glycogen...

Recent advances in the knowledge of parasite biology have unveiled many new antimalarial targets for drug development. The glycogen synthase kinase-3 from Plasmodium falciparum (PfGSK-3) plays an active role in the completion of the asexual erythrocytic stage of P. falciparum life cycle. Due to the limited availability of experimental information (only one dataset is publicly available to our knowledge) and the absence of the target protein structure, the discovery of new inhibitors against PfGSK-3 is quite challenging. Against this background, we have made an effort to develop classification-based (using linear discriminant analysis or LDA) and regression-based quantitative structure-activity relationship (using group based-QSAR or G-QSAR) models for the categorization and quantitative prediction, respectively, of the activity of PfGSK-3 inhibitors. The classification model highlighted the contribution of electronic (Dipole-mag) and topological (S_tsC) descriptors in discriminating the PfGSK-3 inhibitors into more active and less active classes. The regression-based G-QSAR model showed the contribution of fragment-based descriptors (R1-chiV3 and R2-Most-vePotential) in determining the PfGSK-3 inhibitory activity, and also suggested modification sites for the improvement of PfGSK-3 inhibitory activity. The information obtained from this work could be utilized for the identification of novel PfGSK-3 inhibitors with a hope of overcoming the antimalarial resistance problem.