Pharmacophore and 3D-QSAR Characterization of 6-Arylquinazolin-4-amines as Cdc2-like Kinase 4 (Clk4) and Dual Specificity Tyrosine-phosphorylation-regulated Kinase 1A (Dyrk1A) Inhibitors

Abstract

Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) are protein kinases that are promising targets for treatment of diseases caused by abnormal gene splicing. 6-Arylquinazolin-4-amines have been recently identified as potent Clk4 and Dyrk1A inhibitors. In order to understand the structure–activity correlation of these analogs, we have applied ligand-based pharmacophore and 3D-QSAR modeling combined with structure-based homology modeling and docking. The high R2 and Q2 (0.88 and 0.79 for Clk4, 0.85 and 0.82 for Dyrk1A, respectively) based on validation with training and test set compounds suggested that the generated 3D-QSAR models are reliable in predicting novel ligand activities against Clk4 and Dyrk1A. The binding mode identified through docking ligands to the ATP binding domain of Clk4 was consistent with the structural properties and energy field contour maps characterized by pharmacophore and 3D-QSAR models and gave valuable insights into the structure–activity profile of 6-arylquinazolin-4-amine analogs. The obtained 3D-QSAR and pharmacophore models in combination with the binding mode between inhibitor and residues of Clk4 will be helpful for future lead compound identification and optimization to design potent and selective Clk4 and Dyrk1A inhibitors.

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

“…Some of these results are in agreement with those presented by Pan et al [ 34 ] in an atom-based 3D-QSAR modeling study, using this same series of 6-arylquinazolin-4-amines. They observed that the inhibitory activity increases when R 1 is a phenyl ring substituted with a hydrophilic and electron-withdrawing group, R 3 is a heterocyclic ring substituted with a hydrophobic group, and the nitrogen atom of the amine group is substituted with a bulky hydrophobic group.…”

“…They observed that the inhibitory activity increases when R 1 is a phenyl ring substituted with a hydrophilic and electron-withdrawing group, R 3 is a heterocyclic ring substituted with a hydrophobic group, and the nitrogen atom of the amine group is substituted with a bulky hydrophobic group. On the other hand, the inhibitory activity decreases when R 2 is a hydrogen atom and R 1 /R 3 are hydrophobic groups [ 34 ].…”

In-Silico 3D QSAR And Pharmacophore Mapping of C-Kit Inhibitors Towards Anticancer Drug Development

“…The correct pharmacophore model gives the information regarding hydrogen binding properties (acceptor or donor), hydrophobic properties, and aromatic functionality presented by compounds in the dataset. This information could be exploited for the characterization of structurally diverse compounds acting on the same bio-molecules [16].…”

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

“…Additionally, it cannot reflect the quantitative structure-activity relationship (QSAR) as it just considers a single target or a single target-ligand complex [ 17 ]. Compared with structure-based model, ligand-based pharmacophore is more frequently used, which extracts common chemical features from aligned compound structures interacting with the same target, based on the hypothesis that compounds interacting with the same protein target may share similar chemical structure and physicochemical properties [ 18 , 19 ]. The pivotal issues of the ligand-based model are the modeling of ligand flexibility, the alignment methods of molecules and the selection of training set.…”