Title:Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study
Volume: 18
Issue: 13
Author(s): Ashish Gupta, Virender Kumar and Polamarasetty Aparoy*
Affiliation:
- Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh – 176215,India
Keywords:
mPGES-1, Quantitative Structure Activity Relationship (QSAR), Inflammation, Arthritis, Molecular docking,
Structure-activity relationship.
Abstract: Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based
drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations
and accuracy of QSAR models are at large. In this review the applicability of various classes of
molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation
is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as
potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition.
The case study explains the methodology for QSAR analysis, validation of the developed models
and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors.
Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered
in the study. Initially, QSAR models for the training set compounds were developed individually
for each class of molecular descriptors. Further, a combined QSAR model was developed using the best
descriptor from all the classes. The models obtained were further validated using an external test set.
Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental
biological activities of test set compounds. The results of the QSAR analysis were further backed by
docking studies. From the results of the case study it is evident that rather than a single class of molecular
descriptors, a combination of molecular descriptors belonging to different classes significantly improves
the QSAR predictions. The techniques and protocol discussed in the present work might be of significant
importance while developing QSAR models of various drug targets.
