A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

Abstract

A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.

The Effect of Molecular Fields, Lattice Spacing and Analysis Options on CoMFA Predictive Ability

“…Both approaches resulted in equivalent statistical significance for every test and thus only the results of the LOO analyses are detailed below. Inhibitors of angiotensin converting enzyme 114 ACHE [11] Inhibitors of acetyl-cholinesterase 111 AI [25,29] Steroid aromatase inhibitors 78 ARB [30] Nonpeptide angiotensin II receptor antagonists 28 ATA [31] Anti-tuberculosis agents 94 BZR [11] Inhibitors of benzodiazepine receptor 163 CBRA [32] Cannabinoid CB1 receptor agonists 32 COMT [33] Inhibitors of catechol-O-methyltransferase 92 COX2 [11] Inhibitors of cyclooxygenase-2 322 DAT [34] Piperidine analogues for dopamine transporter 42 DHFR [11] Inhibitors of rat dihydrofolate reductase 397 DR [35,36] Antagonists of dopamine receptor 38 ECR [37] Binding of diacylhydrazine to ecdysone receptor 50 EDC [38] Estrogen disrupting chemicals 123 GHS [39] Growth hormone secretagogue mimics 31 GPB [11] Inhibitors of glycogen phosporylase b 66 GSK3B [40] Inhibition of Glycogen synthase kinase 3 42 HIVPR [41] Inhibitors of human immunodeficiency virus protease 113 HIVRT [42,43] Inhibition of HIV-1 reverse transcriptase 101 KOA [44] Kappa opioid antagonists 39 MX [45] Mutagenicity of mutagen X analogues 29 PDE [46] Inhibition of phosphodiesterase-IV 29 PTC [47] Phase-transfer asymmetric catalysts 40 RYR [25,48] Binding of ryanoids to the ryanodine receptor 18 STEROIDS [3,25] Binding of steroids to carrier proteins 21 TCHK [49] Inhibition of trypanosoma cruzi hexokinase 42 THERM [11] Inhibitors of thermolysin 76 THR [11] Inhibitors of thrombin 88 TP2A [50] Inhibition of topoisomerase-IIa 25 YOPH [51] Inhibitors of yersinia protein tyros...…”

Systematic Statistical Comparison of Comparative Molecular Similarity Indices Analysis Molecular Fields for Computer-Aided Lead Optimization

“…The distribution of R cv 2 across the 23 data sets analyzed (Figure 1) was found to vary in a highly statistically significant manner between PLSR models generated using different subsets of CoMSIA molecular fields (Friedman test, 14 inhibitors of acetyl-cholinesterase 111 AI 15 steroid aromatase inhibitors 78 ARB 16 nonpeptide angiotensin II receptor antagonists 28 BZR 14,17 inhibitors of benzodiazepine receptor 163 PTC 18 phase-transfer asymmetric catalysts 40 COMT 19 inhibitors of catechol-O-methyltransferase 92 COX2 14,20 inhibitors of cyclooxygenase-2 322 DAT 21 piperidine analogues for dopamine transporter 42 DHFR 14,22,23 inhibitors of rat dihydrofolate reductase 397 DR 24,25 antagonists of dopamine receptor 38 ECR 26 binding of diacylhydrazine to ecdysone receptor 53 GHS 11 growth hormone secretagogue mimics 31 GPB 14,27 inhibitors of glycogen phosporylase b 66 HIVRT [28][29][30] inhibition of HIV-1 reverse transcriptase 101 HIVPR 10 inhibitors of HIV protease 113 MX 9 mutagenicity of mutagen X analogues 29 PLA2 11,31 indole-based inhibitors of phospholipase A2 11 RYR 30,32 binding of ryanoids to the ryanodine receptor 18 STEROIDS 5,30 binding of steroids to carrier proteins 21 THERM 6,14 inhibitors of thermolysin 76 THR 14,33 inhibitors of thrombin 88 YOPH 34 inhibitors of Yersinia protein tyrosine phosphatase 39 p < 0.001). In general, models built with more molecular fields were more predictive.…”

Investigation of Empirical and Semi‐Empirical Charges to Study the Effects of Partial Charges on Quality and Prediction Accuracy in 3D‐QSAR

“…Many efforts have been made by different research groups to calculate the accurate charge assigning methods. Till date, different studies compared the effect of charges on prediction accuracy on CoMFA and CoMSIA models using single and multiple datasets . and few studies have concentrated on a systematic comparison of prediction accuracy on both training and test set compounds ,.…”