Affiliations: Guru Gobind Singh College of Pharmacy, Yamunanagar, India | Faculty of Pharmaceutical Sciences, Pt. B. D. Sharma
University of Health Sciences, Rohtak, India
Abstract: Antagonism of cannabinoid receptor-1 has emerged as a most promising
therapeutic target for the development of anti-obesity drugs. In the present
study, an in silico approach using decision tree, random forest and
moving average analysis has been applied to a data set comprising of 76
analogues of substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles for development of
models for prediction of antagonistic activity of cannabinoid receptor-1. A
total of 46 2D and 3D molecular descriptors of diverse nature were employed for
decision tree and random forest analysis. The values of majority of these
descriptors for each analogue involved in the dataset were computed using
E-Dragon software (version 1.0). Random forest correctly classified the
analogues into active and inactive with an accuracy of 95%. A decision tree
was also utilized for determining the importance of molecular descriptors. The
decision tree learned the information from the input data with an accuracy of
99% and correctly predicted the cross-validated (10 fold) data with an
accuracy up to 90%. Finally, three molecular descriptors of diverse nature
(including best descriptor identified by decision tree analysis) were
subsequently used to build suitable models using moving average analysis. These
models resulted in the prediction of cannabinoid receptor-1 antagonistic
activity with an accuracy of 95–96%. High predictability of proposed models
offer vast potential for providing lead structures for the development of
potent cannabinoid receptor-1 antagonists for the treatment of obesity.
Keywords: Molecular descriptors, topological descriptors, topochemical descriptors, E-Dragon software, Information content indices, substituted 2-(3-pyrazolyl)-1,3,4-oxadiazoles, moving average analysis, decision tree, random forest, anti-obesity drugs, biarylpyrazolyl oxadiazoles, cannabinoid receptors
