Prediction of inhibition constants of (R)-3-amidinophenylalanine inhibitors toward factor Xa by 2D-QSAR model

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models. The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors. Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA). Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant. The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds. Conclusion: The developed 2D and 3D QSAR models have shown good r2 and q2 values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties. The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

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

BioMed Research International ◽

10.1155/2017/4649191 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 9

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.

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Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors

Data in Brief ◽

10.1016/j.dib.2017.09.036 ◽

2017 ◽

Vol 15 ◽

pp. 281-299 ◽

Cited By ~ 23

Author(s):

Emanuele Amata ◽

Agostino Marrazzo ◽

Maria Dichiara ◽

Maria N. Modica ◽

Loredana Salerno ◽

...

Keyword(s):

Heme Oxygenase ◽

Qsar Model ◽

2D Qsar ◽

Comprehensive Data

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Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity

International Journal of Molecular Sciences ◽

10.3390/ijms22158352 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8352

Author(s):

Magdi E. A. Zaki ◽

Sami A. Al-Hussain ◽

Vijay H. Masand ◽

Manoj K. Sabnani ◽

Abdul Samad

Keyword(s):

External Validation ◽

Factor Xa ◽

Predictive Ability ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Data Set ◽

Qsar Analysis ◽

Lead Compounds ◽

Charged Ring ◽

Life Threatening

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

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Relationship between soybean yield/quality and soil quality in a major soybean-producing area based on a 2D-QSAR model

10.1063/1.4982436 ◽

2017 ◽

Author(s):

Ming Gao ◽

Shiwei Li

Keyword(s):

Soil Quality ◽

Qsar Model ◽

Soybean Yield ◽

2D Qsar ◽

Yield Quality

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Generation of 2D-QSAR Model for Angiogenin Inhibitors: A Ligand-Based Approach for Cancer Drug Design

Trends in Bioinformatics ◽

10.3923/tb.2016.1.13 ◽

2016 ◽

Vol 9 (1) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Krishnan Sundar ◽

Joseph Christina Rosy ◽

Saminathan Balamurali ◽

John Asnet Mary ◽

Rajaiah Shenbagara

Keyword(s):

Drug Design ◽

Qsar Model ◽

Cancer Drug ◽

2D Qsar

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Structure Activity Design, Synthesis and Biological Activity of Newer Imidazole-Triazine Clubbed Derivative as Antimicrobial and Antitubercular Agents

Letters in Organic Chemistry ◽

10.2174/1570178618666210521150011 ◽

2021 ◽

Vol 18 ◽

Author(s):

Jaydeep A. Patel ◽

Navin B. Patel ◽

Pratik K. Maisuriya ◽

Monika R. Tiwari ◽

Amit C. Purohit

Keyword(s):

Thermodynamic Properties ◽

Pharmacological Activity ◽

Predictive Ability ◽

Antitubercular Activity ◽

Qsar Model ◽

Total Surface Area ◽

Lead Target ◽

Antitubercular Agents ◽

Qsar Study ◽

2D Qsar

Background: Imidazole and triazine derivatives act as antimicrobial and antitubercular agents. 2D-QSAR determination estimates the pharmacological activity on the basis of thermodynamic properties of the structure. Objective: The structural arrangements and thermodynamic properties of the imidazole derivatives are necessary for the enhancement of pharmacological activity. So imidazole-triazine clubbed derivatives were designed on the bases of molecular modeling 2D-QSAR study of antitubercular activity. Methods: PLSR method was applied for 2D-QSAR determination of the (Z)-5-ethylidene-3-(4-methoxy-6-methyl-1,3,5-triazin-2-yl)-2-phenyl-3,5-dihydro-4H-imidazol-4-one (B1-B10). The designed compounds were synthesized and spectrally evicted by IR, 1H NMR, 13C NMR, Mass spectra data as well as biologically screened opposite different antitubercular and antimicrobial species. Result: Compounds B4, B6, B7 were founds potent against different antimicrobial species. Compound B3 was more effective against M. tuberculosis H37Rv. Statistically significant QSAR model generated by PLSR methods shows external r2=0.9775 and internal q2=0.2798 predictive ability. Whereas, the model incorporates with three parameters PolarSurfaceAreaExcluding P and S, MomInertiaY and SsCH3count with their corresponding values for each molecule. Conclusion: 2D-QSAR study advised antitubercular activity directly proportional to total surface area of the polar atoms having molecules and moment of inertia on Y-axis. Whereas, inversely proportional to methyl group joined with single bond. The present study afforded favorable results which were further used to generate lead target molecules.

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Sulfonamide Based β-Carbonic Anhydrase Inhibitors: 2D QSAR Study

ISRN Medicinal Chemistry ◽

10.1155/2013/107840 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Meenakshi N. Deodhar ◽

Priyanka L. Khopade ◽

Mahesh G. Varat

Keyword(s):

Carbon Dioxide ◽

Inhibitory Activity ◽

Molecular Design ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Carbonic Anhydrases ◽

Human Pathogens ◽

Qsar Study ◽

Physiological Concentration ◽

2D Qsar

The carbonic anhydrases (CAs) (or carbonate dehydratases) form a family of metalloenzymes that catalyze the rapid interconversion of carbon dioxide and water to bicarbonate and protons (or vice versa), a reversible reaction that occurs rather slowly in the absence of a catalyst. The β-CAs have been characterized in a high number of human pathogens, such as the fungi/yeasts Candida albicans, Candida glabrata, Cryptococcus neoformans, and Saccharomyces cerevisiae and the bacteria Helicobacter pylori, Mycobacterium tuberculosis, Haemophilus influenzae, Brucella suis, and Streptococcus pneumonia. The β-CAs in microorganisms provide physiological concentration of carbon dioxide and bicarbonate (CO2/HCO3-) for their growth. Inhibition of β-CAs from the pathogenic microorganism is recently being explored as a novel pharmacological target to treat infections caused by the these organisms. The present study aimed to establish a relationship between the β-CAs inhibitory activity for structurally related sulphonamide derivatives and the physicochemical descriptors in quantitative terms. The statistically validated two-dimensional quantitative structure activity relationship (2D QSAR) model was obtained through multiple linear regression (MLR) analysis method using Vlife molecular design suits (MDS). Five descriptors showing positive and negative correlation with the β-CAs inhibitory activity have been included in the model. This validated 2D QSAR model may be used to design sulfonamide derivatives with better inhibitory properties.

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Hydrophobic Property of (R)-3 Amidinophenylalanine Inhibitors Contributes to their Inhibition Constants with Thrombin Enzyme

Science and Technology Development Journal ◽

10.32508/stdj.v22i3.1684 ◽

2019 ◽

Vol 22 (3) ◽

Author(s):

Hien Van Nguyen ◽

Van Thi Bich Pham ◽

Hao Minh Hoang

Keyword(s):

Goodness Of Fit ◽

Blood Clot ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Hydrophobic Property ◽

Qsar Modeling ◽

Data Set ◽

Chemical Structures ◽

2D Qsar ◽

Inhibition Constants

Introduction: Thrombin is the key enzyme of fibrin formation in the blood coagulation cascade. Thrombin is released by the hydrolysis of prothrombinase which is generated from factor Xa and factor Va in the presence of calcium ion and phospholipid. The inhibition of thrombin is of therapeutic interest in blood clot treatment. Currently, potent thrombin inhibitors of (R)-3- amidinophenylalanine, derived from benzamidine-containing amino acid, have been developed so far. In order to quantitatively express a relationship between chemical structures and inhibition constants (Ki with thrombin enzyme in a data set of (R)-3-amidinophenylalanine inhibitors), we developed a quantitative structure-activity relationship (QSAR) modeling from a group of 60 (R)-3- amidinophenylalanine inhibitors. Methods: A database containing chemical structures of 60 inhibitors and their Ki values was put into molecular operating environment (MOE) 2008.10 software, and the two-dimensional (2D) physicochemical descriptors were numerically calculated. After removing the irrelevant descriptors, a QSAR modeling was developed from the 2D-descriptors and Ki values by using the partial least squares (PLS) regression method. Results: The results showed that the hydrophobic property, reflected through n-octanol/water partition coefficient (P) of a drug molecule, contributes mainly to Ki values with thrombin.The statistic parameters that give the information about the goodness of fit of a 2D-QSAR model (such as squared correlation coefficient of R2 = 0.791, root mean square error (RMSE) = 0.443, cross-validated Q2 cv = 0.762, and cross-validated RMSEcv = 0.473) were statistically obtained for a training set (60 inhibitors). The R2 and RMSE values were obtained by using a developed model for the testing set (9 inhibitors) ; the total set has statistically significant parameters. Furthermore, the 2D-QSAR modeling was also applied to predict the Ki values of the 69 inhibitors. A linear relationship was found between the experimental and predicted pKi values of the inhibitors. Conclusion: The results support the promising application of established 2D-QSAR modeling in the prediction and design of new (R)-3-amidinophenylalanine candidates in the pharmaceutical industry.

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Identifying the Structural Features of Diphenyl Ether Analogues for InhA Inhibition: A 2D and 3D QSAR Based Study

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190611153933 ◽

2019 ◽

Vol 17 (1) ◽

pp. 31-47 ◽

Cited By ~ 2

Author(s):

Ashutosh Prasad Tiwari ◽

Varadaraj Bhat Giliyar ◽

Gurypur Gautham Shenoy ◽

Vandana Kalwaja Eshwara

Keyword(s):

Inhibitory Activity ◽

Diphenyl Ether ◽

3D Qsar ◽

Qsar Model ◽

Structural Features ◽

Mycolic Acids ◽

2D Qsar ◽

Qsar Models ◽

Mycobacterial Cell Wall ◽

2D And 3D

Background: Enoyl acyl carrier protein reductase (InhA) is a validated target for Mycobacterium. It is an enzyme which is associated with the biosynthesis of mycolic acids in type II fatty acid synthase system. Mycobacterial cell wall majorly comprises mycolic acids, which are responsible for virulence of the microorganism. Several diphenyl ether derivatives have been known to be direct inhibitors of InhA. Objective: In the present work, a Quantitative Structure Activity Relationship (QSAR) study was performed to identify the structural features of diphenyl ether analogues which contribute to InhA inhibitory activity in a favourable way. Method: Both 2D and 3D QSAR models were built and compared. Several fingerprint based 2D QSAR models were generated and their relationship with the structural features was studied. Models which corroborated the inhibitory activity of the molecules with their structural features were selected and studied in detail. Results: A 2D-QSAR model, with dendritic fingerprints having regression coefficient, for test set molecules Q2 =0.8132 and for the training set molecules, R2 =0.9607 was obtained. Additionally, an atom-based 3D-QSAR model with Q2 =0.7697 and R2 =0.9159 was also constructed. Conclusion: The data reported by various models provides guidance for the designing of structurally new diphenyl ether inhibitors with potential activity against InhA of M. tuberculosis.

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