Journal ArticleDOI
Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study.
TLDR
A case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors is discussed to identify crucial physicochemical properties responsible for mPGes-1 inhibition and a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions.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.read more
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Journal ArticleDOI
Prediction of Farnesoid X Receptor Disruptors with Machine Learning Methods.
TL;DR: Five machine learning methods coupled with eight molecular fingerprints and 20 molecular descriptors were used to develop classification models for prediction of FXR binders and the built models could be helpful to rapidly identify potential chemicals binding to FXR.
Journal ArticleDOI
Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives
TL;DR: In this paper, a modeling evaluation of the inhibition of Pin1 using quinazoline, benzophenone, and pyrimidine derivatives was performed by using multilinear, random forest, SMOreg, and IBK regression algorithms on a dataset of 51 molecules, which was divided randomly in 78% for the training and 22 % for the test set.
Book ChapterDOI
Ligand- and Structure-Based Virtual Screening in Drug Discovery
TL;DR: In this article, some of the recent ligand-and structure-based approaches for virtual screening in drug discovery are described and fine tuning of these factors leads to the efficient virtual screening models with high sensitivity.
Journal ArticleDOI
Theoretical investigation on QSAR of (2-Methyl-3-biphenylyl) methanol analogs as PD-L1 inhibitor
TL;DR: In this paper, the quantitative structure-activity relationship (QSAR) established twenty (2-methyl-3-biphenylyl) methanol derivatives as the programmed death ligand-1 (PD-L1) inhibitors.
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