Dragon descriptors

ABSTRACTThe hGPR119 agonistic activity of triazolopyridines has been analysed with topological and molecular features with DRAGON software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 10 descriptors for modelling the activity. The study clearly suggested the role of path/walk 5-Randic shape index (PW5), mean information vertex degree equality (IVDE), Lovasz-Pelikan index (LP1), atomic properties (mass, van der Waals volume and Sanderson electronegativities) in terms of weighted 2D-autocorrelations (MATS4m, MATS2e, MATS4e and MATS5e) and modified Burden eigenvalues (BELm7 and BEHv8) and total primary sp3 hybridized carbon atoms (nCp) in a molecular structure to optimize the hGPR119 agonistic activities of titled compounds. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly. Keywords: QSAR; hGPR119 agonistic activity; Combinatorial protocol in multiple linear regression (CP-MLR) analysis; Dragon descriptors; Triazolopyridine derivatives.