Title:First Report on Two-Fold Classification of Plasmodium falciparum Carbonic Anhydrase Inhibitors Using QSAR Modeling Approaches
VOLUME: 17 ISSUE: 9
Author(s):Rahul Balasaheb Aher and Kunal Roy
Affiliation:Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Keywords:2D-QSAR, linear discriminant analysis, Plasmodium falciparum carbonic anhydrase, two-fold classification.
Abstract:Quantitative structure-activity relationship (QSAR)-based classification approach is one of the important
chemometric tools in drug discovery process for categorizing the target protein inhibitors into more active and less active
classes. In this background, we have presented here a novel approach of two-fold QSAR-based classification modeling for
the Plasmodium falciparum carbonic anhydrase (PfCA) inhibitors using 2D-QSAR and linear discriminant analysis
(LDA) methods. The logic of applying this concept is to ensure more accurate classification of compounds and to draw
some concrete conclusion about structure-activity relations for further work, in absence of 3D-protein structure and lack
of sufficient experimental data using the PfCA target. The 2D-QSAR modeling analysis suggested the importance of
electrotopological, electronic, extended topochemical atom, and spatial (Jurs) indices for modeling the inhibitory activity
against PfCA. The LDA model analysis showed that spatial (Jurs), electrotopological and thermodynamic indices were the
discriminating features to differentiate the inhibitors into more active and less active groups. The classification ability of
both the models for training and test sets was checked by different qualitative validation parameters such as sensitivity,
specificity, accuracy, recall, precision, F-measure and G-means. The classification results revealed that the developed
models were significant in classifying the more active inhibitors as compared to the less active inhibitors of both training
and test sets. The structural features unveiled from these two models could be utilized for the selection of more active
compounds against PfCA in the database screening process.
