Linear and nonlinear QSAR modeling of 1,3,8-substituted-9-deazaxanthines as potential selective A2BAR antagonists

Mansourian, Mahboubeh; Saghaie, Lotfollah; Fassihi, Afshin; Madadkar-Sobhani, Armin; Mahnam, Karim

doi:10.1007/s00044-012-0453-8

Cited by 10 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the receptor structure is not available, the information which the ligands carry sheds light on the drug design and discovery plans. Defining statistical models which describe the dependence of the biological properties to the structural features of the bioactive compounds, quantitative structure activity relationship (QSAR) studies, are the most known examples of ligand-based approaches (Sabet et al, 2012;Mansourian et al, 2013;Macchiarulo et al, 2004;Mishra et al, 2014). Each of these approaches has advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

et al. 2014

Self Cite

View full text Add to dashboard Cite

Inhibition of gp41 protein was proposed as a possible mechanism for the anti-HIV-1 properties of some 4-aryl-1,2,3,4-tetrahydropyrimidine-2(1H)-one (thione) derivatives. Two different in silico approaches, namely quantitative structure activity relationship (QSAR) and molecular docking studies were performed to characterize the relation between the structural features and the anti-HIV-1 activity and investigating the mode of interaction of the compounds with gp41. In the QSAR approach, free least squares support vector machine was used to derive a non-linear model based on the most important descriptors responsible for the activity of the compounds selected by stepwise multiple linear regression method. Docking results proved that the studied molecules have the optimum key interactions including hydrogen bonding, hydrophobic, electrostatic, p-p and cation-p interactions with the specific inhibitor binding site of gp41.

show abstract

Section: Introductionmentioning

confidence: 99%

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering a similar definition as shown in Eqs. (10) and (11), the inward-inverse of the vector � � � � ⟩ � noted and defined as: can be easily described, and its shadow written as:…”

Section: The Inverse Of a Vectormentioning

confidence: 99%

“…In the present study the connection of the structure of vectors spaces, developed in parallel to the reflections about the so-called classical quantitative structure-properties relations (QSPR), 1 see for example an assorted set of Refs. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], will be The present writing is dedicated to Blanca Cercas, my wife. Without her love, heartful care and understanding, some past and most actual work could not have been done so smoothly.…”

Section: Introductionmentioning

confidence: 99%

Shadows’ hypercube, vector spaces, and non-linear optimization of QSPR procedures

Carbó‐Dorca

2021

J Math Chem

View full text Add to dashboard Cite

The role of the shadows' hypercube is presented first to define a classification of the vectors in a vector space defined over the rational field. Up from this step, the inward product of vectors is presented as the basis for non-linear optimization of several vector scalar and matrix functions. The first use of the inward power of a vector shows that the simple least-squares fitting can be non-linearly optimized. Then the shadows' hypercube is connected with the topological description of molecules. Further application of the developed theoretical background to the QSPR problem permits us to have some insight into the role of descriptor representation of molecular structures and the non-linear optimization of the involved equations. KeywordsShadows' hypercube • Inward vector product • Inward power of a vector • Complete vector sum • Collective vector scalar products • Similarity of a vector set • Least squares problem • Non-linear optimization • Topological matrices • Discrete representation of molecules • QSPR • QSAR

show abstract

“…A three-dimensional (3D) structure of the Pig Gastric H + /K + ATPase receptor with 2XZB PDB code was obtained at 7 Å resolution from http://www.pdb.org [28]. The 2D structures of cimetidine and three main components including para-cymene, gamma-terpinene a n d t h y m o l w e r e d r a w n a n d o p t i m i z e d u s i n g HyperChem 7.0 software (version 7.0; Hypercube, Inc., Gainesville, FL, USA; http://www.hyper.com) as described previously [29]. Using the MM + molecular mechanical force field, 3D geometry optimization calculations for each ligand performed.…”

Section: Molecular Docking Of the Main Components Of T Ammi Essentiamentioning

confidence: 99%

“…Using the MM + molecular mechanical force field, 3D geometry optimization calculations for each ligand performed. The ultimate conformations were calculated with the Semi-empirical AM1 method using Polak-Ribiere algorithm in Hyperchem software until the root mean square gradient was 0.01 kcal/mol/Å [29]. Each compound was docked into the enzyme-binding pocket applying the routine procedure and default parameters of the software.…”

Section: Molecular Docking Of the Main Components Of T Ammi Essentiamentioning

confidence: 99%

Trachyspermum ammi (L.) Sprague, superb essential oil and its major components on peptic ulcers: in vivo combined in silico studies

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background Trachyspermum ammi (L.) Sprague is used for treating gastrointestinal disorders. Several studies indicated gastric antiulcer activity of T. ammi extract, yet the effect of its essential oil has not been studied on. Objectives The present study evaluates chemical composition of T. ammi essential oil and anti-peptic ulcer effect of the essential oil as well as its three major components in ethanol induced-gastric ulcers in rats. Methods Primarily chemical composition of the essential oil was analyzed by gas chromatography-mass spectrometry (GC/MS). Rats received the essential oil (500, 250, 125, 62.5, 31.25 mg/kg), thymol (30, 100 mg/kg), para-cymene (100, 150 mg/kg) and gamma-terpinene (100, 150 mg/kg) using gavage tube along with ethanol 80%. Finally, dissected stomachs were assessed both macroscopically and microscopically to evaluate anti-ulcerative effect of the essential oil and the pure compounds. Moreover, molecular docking was utilized to explore the interactive behavior of the main components with active site residues of H + /K + ATPase. Results Analysis of the essential oil indicated that para-cymene (37.18%), gamma-terpinene (35.36%) and thymol (20.51%) are the main components. Administration of different doses of the essential oil noticeably diminished the number of peptic ulcers in a dose-dependent manner. Among the main components, thymol was more potent than para-cymene and gamma-terpinene. Administration of the essential oil (500 mg/kg) and thymol (100 mg/kg) observed maximum inhibition percentage (98.58% and 79.37%, respectively). Molecular docking study provides the evidence of thymol ability to inhibit H + /K + ATPase. Conclusions The findings revealed that T. ammi essential oil can be applied to treat gastric ulcer as a natural agent.

show abstract

Linear and nonlinear QSAR modeling of 1,3,8-substituted-9-deazaxanthines as potential selective A2BAR antagonists

Cited by 10 publications

References 40 publications

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

Shadows’ hypercube, vector spaces, and non-linear optimization of QSPR procedures

Trachyspermum ammi (L.) Sprague, superb essential oil and its major components on peptic ulcers: in vivo combined in silico studies

Contact Info

Product

Resources

About