Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D‐QSAR Analysis

Abstract

In the present work, 2D-and 3D-quantitative structure-activity relationship (QSAR) analysis has been employed for a diverse set of eighty-nine quinoxalinones to identify the pharmacophoric features with significant correlation with the aldose reductase inhibitory activity. Using genetic algorithm (GA) as a variable selection method, multivariate linear regression (MLR) models were derived using a pool of molecular descriptors. All the sixdescriptor based GA-MLR QSAR models are statistically robust with coefficient of determination (R 2 ) > 0.80 and cross-validated R 2 > 0.77. The derived GA-MLR models were thoroughly validated using internal and external and Yscrambling techniques. The CoMFA like model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, is highly robust with R 2 > 0.93 and cross-validated R 2 > 0.73. The established QSAR and CoMFA like models are proficient in identify key pharmacophoric features that govern the aldose reductase inhibitory activity of quinoxalinones.Keywords: Aldose Reductase Activity · Quinoxalinones · QSAR · CoMFA like model [a] V.

QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors

“…Therefore, the standard procedure has been followed to derive a balanced QSAR model for inhibitory activity of Nitrogen heterocycles for Human NMT. More details about the procedure followed in the present work are available in the literature [ 11 , 17 , 21 , 22 , 23 , 24 , 25 ].…”

“…In addition, Williams plot was used to assess the applicability domain of the developed QSAR model. To add further, the following rules were used to select and validate a model [ 11 , 17 , 22 , 23 , 24 ]: R2tr ≥ 0.6, Q2loo ≥ 0.5, Q2LMO ≥ 0.6, R2 > Q2, R2ex ≥ 0.6, RMSEtr < RMSEcv, ΔK ≥ 0.05, CCC ≥ 0.80, Q2-Fn ≥ 0.60, r2m ≥ 0.5, (1-r2/ro2) < 0.1, 0.9 ≤ k ≤ 1.1 or (1-r2/r’o2) < 0.1, 0.9 ≤ k’ ≤ 1.1,| ro2− r’o2| < 0.3 with RMSE and MAE as low as possible. The Q2LMO value reported herein is mean value of 2000 repetitions with 30% of the objects randomly excluded from the training set each time.…”

“…In addition, Williams plot was used to assess the applicability domain of the developed QSAR model. To add further, the following rules were used to select and validate a model [11,17,[22][23][24]…”

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

“…The statistical quality and strength of a GA-MLR based QSAR model was judged on the basis of: (a) internal validation based on leave-one-out (LOO) and leave-many-out (LMO) procedure (i.e. cross-validation (CV)); (b) using External validation; (c) Yrandomization (or Y-scrambling) and (d) fulfilling of respective threshold value for the statistical parameters (Masand et al, 2019a;Masand et al, 2018;Masand et al, 2019b):…”

“…The statistical quality and strength of a GA-MLR based QSAR model was judged on the basis of: (a) internal validation based on leave-one-out (LOO) and leave-many-out (LMO) procedure (i.e. cross-validation (CV)); (b) using External validation; (c) Y-randomization (or Y-scrambling) and (d) fulfilling of respective threshold value for the statistical parameters [ 31 , 32 ]: R 2 tr ​≥ ​0.6, Q 2 loo ​≥ ​0.5, Q 2 LMO ​≥ ​0.6, R 2 ​> ​ Q 2 , R 2 ex ​≥ ​0.6, RMSE tr ​< ​ RMSE cv , ΔK ​≥ ​0.05, CCC ​≥ ​0.80, Q 2 - F n ​≥ ​0.60, r 2 m ​≥ ​0.6, (1- r 2 / r o 2 ) ​< ​0.1, 0.9 ​≤ ​ k ​≤ ​1.1 or (1- r 2 / r ’ o 2 ) ​< ​0.1, 0.9 ​≤ ​ k ’ ​≤ ​1.1,| r o 2 − r ’ o 2 | ​< ​0.3 with RMSE and MAE close to zero. A QSAR model that did not satisfy above mentioned criteria was consequently excluded.…”