Multicomplex‐Based Pharmacophore‐Guided 3D‐QSAR Studies of N‐Substituted 2′‐(Aminoaryl)Benzothiazoles as Aurora‐A Inhibitors

Abstract

Aurora‐A has been known as one of the most important targets for cancer therapy, and some Aurora‐A inhibitors have entered clinical trails. In this study, combination of the ligand‐based and structure‐based methods is used to clarify the essential quantitative structure–activity relationship of known Aurora‐A inhibitors, and multicomplex‐based pharmacophore‐guided method has been suggested to generate a comprehensive pharmacophore of Aurora‐A kinase based on a collection of crystal structures of Aurora‐A–inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 37 structurally diverse N‐substituted 2′‐(aminoaryl)benzothiazoles derivatives. The quantitative structure–activity relationship analyses have been performed on these Aurora‐A inhibitors based on multicomplex‐based pharmacophore‐guided alignment. These results may provide important information for further design and virtual screening of novel Aurora‐A inhibitors.

Novel Hybrid Virtual Screening Protocol Based on Molecular Docking and Structure-Based Pharmacophore for Discovery of Methionyl-tRNA Synthetase Inhibitors as Antibacterial Agents

“…We assumed that the pharmacophore features present in the complexes with a high probability were more important than features exhibiting low probability. For a full pharmacophore map, excluded volume features should be included, which reflected potential steric restriction and corresponded to positions that were inaccessible to any potential ligand [18]. Twenty-six excluded volume features were found in the ATP-binding and methionine-binding sites, whose spaces were occupied by residues Pro247, Ile248, Tyr250, Asp287, His289, Gly290, Glu368, Val471, Tyr472, Val473, Trp474, Asp476, Ala477, Leu478, Tyr481, Ile519 and His523.…”

“…The combined structure- and ligand-based drug design strategy provided insights into the molecular recognition patterns required for MetRS binding and for developing a structure-based pharmacophore model (MCBP) that can be used for VS to discover novel potential lead compounds [18–23]. The structure-based pharmacophore and VS results helped us predict the biological activities of the series compounds with a change in the chemical substitutions and provided useful references for the design of novel MetRS inhibitors.…”

Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity

“…The crystal structures with FBP, the natural ligand of PKM2, were not used in the analyses in order to avoid the unnecessary noise likely to be introduced into the pharmacophore model 31. The generation procedures of the structure-based pharmacophore models were referenced to our previous reports 22–26. The whole process of generation and utilization of the structure-based pharmacophore models were illustrated in Figure 2 and detailed as follows.…”

“…Our ongoing research aimed to search specific PKM2 activators21 and to explore techniques to generate more accurate and reasonable structure-based computer-aided drug design methods 22–29. Structure-based pharmacophore (SBP) design and hybrid protocol of virtual screening can be used to detect novel tetrahydroquinoline-based lead compounds based on changes in the chemical scaffold and can provide useful references for the design and preliminary evaluation of PKM2 activators.…”

A combination of 2D similarity search, pharmacophore, and molecular docking techniques for the identification of vascular endothelial growth factor receptor-2 inhibitors

“…A comprehensive pharmacophore map with ligand-binding conformation was too restrictive and not suitable for virtual screening because it contained several chemical features; thus, a molecule fit to such a pharmacophore model was not possible [27]. A reduced pharmacophore features model was more practical [28,29]. Consequently, the top-ranked seven properties, that is -A1, A2, D2, H2, H3, H4, and H5 were selected from the comprehensive pharmacophore model and merged to generate a pharmacophore modeling on the basis of receptor-ligand interactions (Fig.…”