Novel TOPP descriptors in 3D-QSAR analysis of apoptosis inducing 4-aryl-4H-chromenes: Comparison versus other 2D- and 3D-descriptors

2007 ◽  
Vol 15 (19) ◽  
pp. 6450-6462 ◽  
Author(s):  
Simone Sciabola ◽  
Emanuele Carosati ◽  
Lourdes Cucurull-Sanchez ◽  
Massimo Baroni ◽  
Raimund Mannhold
Keyword(s):  
3D Qsar ◽  
Qsar Analysis ◽  
3D Descriptors ◽  
2D And 3D

Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D‐QSAR Analysis

2019 ◽  
Vol 38 (8-9) ◽  
pp. 1800149
Author(s):  
Vijay H. Masand ◽  
Nahed N. Elsayed ◽  
Sumersingh D. Thakur ◽  
Nandkishor Gawhale ◽  
Mithilesh M. Rathore
Keyword(s):  
Aldose Reductase ◽  
3D Qsar ◽  
Qsar Analysis ◽  
Aldose Reductase Inhibitors ◽  
Reductase Inhibitors ◽  
2D And 3D

scholarly journals 2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

2019 ◽  
pp. 104-114
Author(s):  
Smita Suhane ◽  
A. G. Nerkar ◽  
Kumud Modi ◽  
Sanjay D. Sawant
Keyword(s):  
Factor Xa ◽  
3D Qsar ◽  
Qsar Model ◽  
Regression Method ◽  
Field Analysis ◽  
Factor Xa Inhibitors ◽  
Nearest Neighbour ◽  
Qsar Analysis ◽  
Qsar Models ◽  
2D And 3D

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.

Structural requirements of 3-carboxyl-4(1H)-quinolones as potential antimalarials from 2D and 3D QSAR analysis

2013 ◽  
Vol 44 ◽  
pp. 266-277 ◽  
Author(s):  
Jiazhong Li ◽  
Shuyan Li ◽  
Chongliang Bai ◽  
Huanxiang Liu ◽  
Paola Gramatica
Keyword(s):  
3D Qsar ◽  
Structural Requirements ◽  
Qsar Analysis ◽  
2D And 3D

scholarly journals Exploring structure indenture for some Schiff bases as anti-Salmonella typhi drugs: A QSAR Approach

2016 ◽  
Vol 2 (2) ◽  
pp. 48
Author(s):  
Ameji John ◽  
Awe Emmanuel Femi ◽  
Adedirin Oluwaseye ◽  
Olusupo Sabitu
Keyword(s):  
Schiff Bases ◽  
Molecular Size ◽  
Salmonella Typhi ◽  
Qsar Analysis ◽  
Robust Model ◽  
3D Descriptors ◽  
2D And 3D ◽  
Size Descriptor ◽  
Insight Into ◽  
Dominant Influence

There are many drugs available in the market for treating typhoid infection, but the emergence of multi-drug resistant strain of Salmonella typhi (S.typhi) has necessitated the exploration and development of newer structural moiety of Schiff bases as anti-S. typhi agents owing to their enormous inhibitory activity against this bacterium. In this present study, a Genetic function approximation (GFA) QSAR analysis of some selected Schiff bases with anti-S. typhi activity was performed using OD,1D, 2D and 3D descriptors resulting in the generation of three statistically significant models from which an octa-parametric model was selected as the most robust model with R2 = 0.8589, R2adj = 0.8155, Q2 = 0.7437,R2 - Q2 = 0.1152, r2 r02 / r2 =0.00, r2 r02 / r2 = 0.0263, K = 1.0E-7, K = 0.1969. The optimization model hinted the dominant influence of the size descriptor ETA-Eta-B (Branching index EtaB relative to molecular size) on the observed anti-S.typhi activity of Schiff bases. It is envisaged that the QSAR results identified in this study will offer important structural insight into designing novel anti-S.typhi drugs from Schiff bases.

2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA

2016 ◽  
Vol 11 (4) ◽  
pp. 292-303 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Hamid Shiri ◽  
Alireza Banaei ◽  
Mohammad Ganjali
Keyword(s):  
Kinase Inhibitors ◽  
3D Qsar ◽  
Qsar Analysis ◽  
2D And 3D ◽  
Egfr Kinase

2D- and 3D-QSAR and Molecular Docking of 2-Hydroxyisoquinoline-1,3-Diones as Inhibitors of HIV Reverse Transcriptase

2020 ◽  
Vol 5 (4) ◽  
pp. 32-52
Author(s):  
Ilham Aanouz ◽  
Khalil El Khatabi ◽  
Assia BelHassan ◽  
Tahar Lakhlifi ◽  
Mostafa Elidrissi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Molecular Similarity ◽  
Similarity Index ◽  
Principal Component ◽  
3D Qsar ◽  
Hiv Reverse Transcriptase ◽  
Training Set ◽  
Qsar Analysis ◽  
Contour Maps ◽  
2D And 3D

The main objective of this study is to develop 2D- and 3D-QSAR statistical models for a series consisting of 28 molecules. The authors started with a 2D study based on principal component analysis (PCA), multiple linear regression (RLM), and nonlinear regression (RNLM). The models were developed using 28 molecules with a pIC50 between 5.70 and 6.70. Then they applied 3D-QSAR analysis based on the partial least squares (PLS) method. For 3D-QSAR, they used the molecular field comparative analysis (CoMFA) and comparative molecular similarity index (CoMSIA) methods. For this analysis, they have worked on a training set of 24 compounds, which then give acceptable and reliable values of Q2 (0.791 and 0.538, respectively) and R2 (0.974 and 0.98, respectively). To determine the quantitative 3D-QSAR, the interpretations were based on the contour maps which are produced by the CoMFA and CoMSIA models. In addition, molecular docking is also one of the most important methods for confirming the binding interactions of predicted molecules with their receptors.

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