Der Pharma Chemica
Journal for Medicinal Chemistry, Pharmaceutical Chemistry, Pharmaceutical Sciences and Computational Chemistry

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Abstract

2D QSAR Analysis on 5β-Methylprolyl-2-Cyanopyrrolidine Derivatives as DPP IV Inhibitors

Author(s): Sanmati K. Jain, Sarika Vishwakarma and Pragya Nayak

Two dimensional quantitative structure activity relationship (2D QSAR) study was performed on 5β-methylprolyl-2-cyanopyrrolidine derivatives as dipeptidyl peptidase IV (DPP IV) inhibitors using molecular design suite software (VLifeMDS). This study was performed with 30 compounds (data set) using sphere exclusion (SE) algorithm, random and manual selection methods for the division of the data set into training and test set. Partial least square (PLS) linear regression analysis coupled with stepwise variable selection method was applied to derive QSAR models which were further validated for statistical significance by internal and external validation. The most significant model has squared correlation coefficient (r2), cross validated correlation coefficient (q2) and predictive correlation coefficient (pred_r2) 0.6231, 0.5109 and 0.3862 respectively. The QSAR model indicates that the descriptors T_C_O_4 [This is the count of number of Carbon atoms (single, double or triple bonded) separated from Oxygen atom (single or double bonded) by 4 bond distance in a molecule], SdssCE-index [Electrotopological state indices for number of carbon atom connected with two single bonds] and XlogP [This descriptor signifies ratio of solute concentration in octanol & water and generally termed as octanol water partition coefficient] contributing 48.19%, 28.93% and 22.88 % respectively. Negative coefficient value of T_C_O_4 and XlogP indicated that lower value leads to better dipeptidyl peptidase inhibitory activity whereas higher value leads to decrease activity whereas positive coefficient value of SdssCE-index indicated that higher value leads to good dipeptidyl peptidase inhibitory activity while lower value leads to reduced activity.


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