scholarly journals Rational Design of Novel Inhibitors of α-Glucosidase: An Application of Quantitative Structure Activity Relationship and Structure-Based Virtual Screening

2021 ◽  
Vol 14 (5) ◽  
pp. 482
Author(s):  
Sobia Ahsan Halim ◽  
Sumaira Jabeen ◽  
Ajmal Khan ◽  
Ahmed Al-Harrasi
Keyword(s):  
Diabetes Mellitus ◽  
Virtual Screening ◽  
Correlation Coefficient ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Inhibitory Activities

α-Glucosidase is considered a prime drug target for Diabetes Mellitus and its inhibitors are used to delay carbohydrate digestion for the treatment of diabetes mellitus. With the aim to design α-glucosidase inhibitors with novel chemical scaffolds, three folds ligand and structure based virtual screening was applied. Initially linear quantitative structure activity relationship (QSAR) model was developed by a molecular operating environment (MOE) using a training set of thirty-two known inhibitors, which showed good correlation coefficient (r2 = 0.88), low root mean square error (RMSE = 0.23), and cross-validated correlation coefficient r2 (q2 = 0.71 and RMSE = 0.31). The model was validated by predicting the biological activities of the test set which depicted r2 value of 0.82, indicating the robustness of the model. For virtual screening, compounds were retrieved from zinc is not commercial (ZINC) database and screened by molecular docking. The best docked compounds were chosen to assess their pharmacokinetic behavior. Later, the α-glucosidase inhibitory potential of the selected compounds was predicted by their mode of binding interactions. The predicted pharmacokinetic profile, docking scores and protein-ligand interactions revealed that eight compounds preferentially target the catalytic site of α-glucosidase thus exhibit potential α-glucosidase inhibition in silico. The α-glucosidase inhibitory activities of those Hits were predicted by QSAR model, which reflect good inhibitory activities of these compounds. These results serve as a guidelines for the rational drug design and development of potential novel anti-diabetic agents.

scholarly journals 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Manman Zhao ◽  
Lin Wang ◽  
Linfeng Zheng ◽  
Mengying Zhang ◽  
Chun Qiu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Egfr Inhibitors ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
2D Qsar ◽  
Structure Activity

Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient) and r2=0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.

scholarly journals VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR

2017 ◽  
Vol 10 (12) ◽  
pp. 150
Author(s):  
Shobana Sugumar
Keyword(s):  
Virtual Screening ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Pharmacophore Modeling ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Hydrogen Bond Acceptor ◽  
Structure Activity

  Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score, and with that, a five-point pharmacophore model was developed consisting of two hydrogen bond acceptor and three ring atoms, and the pharmacophore hypothesis yielded a statistically significant three-dimensional QSAR (3D-QSAR) model with a correlation coefficient of r2=0.8962 as well as good predictive power.Conclusion: The pharmacophore-based 3D-QSAR model generated from natural antihistamines can provide intricate structural knowledge about a new class of anti-allergic and anti-inflammatory drug research.

scholarly journals Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Toshio Kasamatsu ◽  
Airi Kitazawa ◽  
Sumie Tajima ◽  
Masahiro Kaneko ◽  
Kei-ichi Sugiyama ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Ames Test ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Low Molecular Weight ◽  
Quantitative Structure ◽  
Industrial Chemicals ◽  
Structure Activity

Abstract Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis

RSC Advances ◽  
2015 ◽  
Vol 5 (70) ◽  
pp. 57030-57037 ◽  
Author(s):  
Arafeh Bigdeli ◽  
Mohammad Reza Hormozi-Nezhad ◽  
Hadi Parastar
Keyword(s):  
Gold Nanoparticles ◽  
Gold Nanoparticle ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Determining Factors

A nano-quantitative structure-activity relationship (nano-QSAR) model is proposed to indicate the determining factors responsible in the exocytosis of gold nanoparticles in macrophages.

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP MODELING FOR PREDICTION OF PYRIDINE SUBSTITUTED ANALOGUES WITH K-NEAREST NEIGHBOR STUDIES

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (10) ◽  
pp. 16-22
Author(s):  
M. C. Sharma ◽  
◽  
D.V. Kohli
Keyword(s):  
Correlation Coefficient ◽  
Quantitative Relationship ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Field Analysis ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Angiotensin Ii Receptor ◽  
K Nearest Neighbor ◽  
Structure Activity

A quantitative structure activity relationship study was performed on a series of imidazo[4,5-b]pyridine substituted compounds as angiotensin II receptor antagonists for establishing quantitative relationship between activity and their physicochemical properties. The best quantitative structure activity relationship model was generated with correlation coefficient of 0.8318, cross validated correlation coefficient of 0.7142 and r2 for external test set 0.7965. Molecular field analysis was used to construct the best 3D-QSAR model using PLS method, showing good correlative and predictive capabilities in terms of q2 = 0.7264 and pred_r2 = 0.8164. These results will be useful for the design of new antihypertensive molecules.

scholarly journals Rational Design of Novel Anti-microtubule Agent (9-Azido-Noscapine) from Quantitative Structure Activity Relationship (QSAR) Evaluation of Noscapinoids

2011 ◽  
Vol 16 (9) ◽  
pp. 1047-1058 ◽  
Author(s):  
Seneha Santoshi ◽  
Pradeep K. Naik ◽  
Harish C. Joshi
Keyword(s):  
Rational Design ◽  
Lymphoblastic Leukemia ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Nuclear Magnetic Resonance Analysis ◽  
Quantitative Structure ◽  
Structural Framework ◽  
Structure Activity

An anticough medicine, noscapine [(S)-3-((R)4-methoxy-6-methyl-5,6,7,8-tetrahydro-[1,3]dioxolo[4,5-g]isoquinolin-5-yl)-6,7-dimethoxyiso-benzofuran-1(3H)-one], was discovered in the authors’ laboratory as a novel type of tubulin-binding agent that mitigates polymerization dynamics of microtubule polymers without changing overall subunit-polymer equilibrium. To obtain systematic insight into the relationship between the structural framework of noscapine scaffold and its antitumor activity, the authors synthesized strategic derivatives (including two new ones in this article). The IC50 values of these analogs vary from 1.2 to 56.0 µM in human acute lymphoblastic leukemia cells (CEM). Geometrical optimization was performed using semiempirical quantum chemical calculations at the 3-21G* level. Structures were in agreement with nuclear magnetic resonance analysis of molecular flexibility in solution and crystal structures. A genetic function approximation algorithm of variable selection was used to generate the quantitative structure activity relationship (QSAR) model. The robustness of the QSAR model ( R2 = 0.942) was analyzed by values of the internal cross-validated regression coefficient ( R2LOO = 0.815) for the training set and determination coefficient ( R2test = 0.817) for the test set. Validation was achieved by rational design of further novel and potent antitumor noscapinoid, 9-azido-noscapine, and reduced 9-azido-noscapine. The experimentally determined value of pIC50 for both the compounds (5.585 M) turned out to be very close to predicted pIC50 (5.731 and 5.710 M).

scholarly journals QUANTUM CHEMICAL STUDY FOR THE TOXICITY PREDICTION OF SULFONAMIDE ANTIBIOTICS WITH QUANTITATIVE STRUCTURE – ACTIVITY RELATIONSHIP

2020 ◽  
Vol 51 (1) ◽  
pp. 7-13
Author(s):  
S. Aydogdu ◽  
Arzu Hatipoglu
Keyword(s):  
Correlation Coefficient ◽  
Quantum Chemical ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Structure Activity ◽  
Qsar Models ◽  
Chemical Descriptors

Sulfonamides are one of the most important classes of chemicals found in the aquatic environment as a pollutant due to excessive consumption. The DFT- B3LYP method with the basis set 6-311++G (d,p) was employed to calculate various quantum chemical descriptors of sulfonamide molecules. A quantitative structure activity relationship (QSAR) study was performed for the toxicity value LD50 of sulfonamides with their quantum chemical descriptors by multi linear regression. The QSAR models were validated by internally and externally. The best multilinear equation with correlation coefficient, R and the cross-validation leave-one-out correlation coefficient, Q2 values were 0.9528 ,0.8556 respectively The results show that the QSAR models have both favourable estimation stability and good prediction power.

STRUCTURAL FEATURE STUDY OF FUROXAN DERIVATIVES AS POTENT ANTI-TRYPANOSOMATID AGENTS: QSAR APPROACH

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (09) ◽  
pp. 12-21
Author(s):  
M. C. Sharma ◽  
Keyword(s):  
Cross Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Property A ◽  
Qsar Studies ◽  
Structure Activity

Two-dimensional quantitative structure–activity relationship (QSAR) studies of anti-trypanosomatid, furoxan alkylnitrate derivatives have been carried out. This study aims at establishing a quantitative structure activity relationship between furoxan alkylnitrate molecule and their anti-trypanosomatid property. A statistically best QSAR model was obtained with a correlation coefficient r2 of 0.8559, cross validation coefficient, q2 of 0.8072 and pred_r2 value of 0.8217. Various 2D descriptors were calculated and used in the present analysis. The descriptors SdssS (sulfone) count and SdsNE-index suggested that sulphone and NO2 groups at the R1 and R2 positions of furoxan moiety will increases anti-trypanosomatid activity. It will be useful to build a QSAR model to correlate the properties of new untested furoxan derivatives with their anti-trypanosomatid activity.

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