Hologram QSAR Models of a Series of 6-Arylquinazolin-4-Amine Inhibitors of a New Alzheimer’s Disease Target: Dual Specificity Tyrosine-Phosphorylation-Regulated Kinase-1A Enzyme

Abstract

Dual specificity tyrosine-phosphorylation-regulated kinase-1A (DYRK1A) is an enzyme directly involved in Alzheimer’s disease, since its increased expression leads to β-amyloidosis, Tau protein aggregation, and subsequent formation of neurofibrillary tangles. Hologram quantitative structure-activity relationship (HQSAR, 2D fragment-based) models were developed for a series of 6-arylquinazolin-4-amine inhibitors (36 training, 10 test) of DYRK1A. The best HQSAR model (q2 = 0.757; SEcv = 0.493; R2 = 0.937; SE = 0.251; R2pred = 0.659) presents high goodness-of-fit (R2 > 0.9), as well as high internal (q2 > 0.7) and external (R2pred > 0.5) predictive power. The fragments that increase and decrease the biological activity values were addressed using the colored atomic contribution maps provided by the method. The HQSAR contribution map of the best model is an important tool to understand the activity profiles of new derivatives and may provide information for further design of novel DYRK1A inhibitors.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls

“…This method requires no explicit 3D information (e.g., determination of 3D structures, putative binding conformations, and molecular structural alignment) and avoids the calculation and selection of physicochemical descriptors. Only 2D structures of the data set compounds and the corresponding property values are required for the HQSAR application (Salum and Andricopulo, 2009;Leal et al, 2015). Therefore, Dragon-based QSPR and HQSAR were used in this study.…”

Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure–Activity Relationship (HQSAR) Modeling of Amino–Imino Tautomers

“…The limitation of the predictive ability of the best models for the tautomers I and II datasets was determined by the presence of outliers, i.e., compounds of the training and test sets showing residual values exceeding twice the standard deviation (SD) of estimate of the model (tautomer I, 2 * SD = 0.47; tautomer II, 2 * SD = 0.38) [ 17 , 18 , 27 ].…”

“…In the HQSAR method, the occurrence of each 2D molecular fragment derived from each molecule in the dataset forms a set of molecular (hologram or fingerprint) descriptors (X, independent variables), which are correlated to the biological activity response (Y, dependent variable) [ 16 , 17 , 18 , 19 ].…”

In Silico Studies in Drug Research Against Neurodegenerative Diseases

“…Leal et al . [141] developed local HQSAR models of a series of 46 6-arylquinazolin-4-amine DYRK1A inhibitors using the commercial SYBYL software to find structural fragments with favorable contribution to the inhibitory activity. The color of each molecular fragment provided information about contribution of each molecular fragments to the inhibitory activity of this series where the yellow-to-green color exhibited the positive contribution and orange-to-red colors indicated negative contributions of the fragments of the molecules under study.…”