Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis

1990 ◽  
Vol 33 (9) ◽  
pp. 2583-2590 ◽  
Author(s):  
Tomoo Aoyama ◽  
Yuji Suzuki ◽  
Hiroshi Ichikawa
Keyword(s):  
Neural Networks ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity

scholarly journals Quantitative Structure Activity Relationship (QSAR) Based on Electronic Descriptors and Docking Studies of Quinazoline Derivatives for Anticancer Activity

2018 ◽  
Vol 34 (5) ◽  
pp. 2361-2369
Author(s):  
Herlina Rasyid ◽  
Bambang Purwono ◽  
Ria Armunanto
Keyword(s):  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity ◽  
B3lyp Method ◽  
Quinazoline Derivatives

Quantitative structure-activity relationship (QSAR) based on electronic descriptors had been conducted on 2,3-dihydro-[1,4]dioxino[2,3-f]quinazoline analogues as anticancer using DFT/B3LYP method. The best QSAR equation described as follow: Log IC50 = -11.688 + (-35.522×qC6) + (-21.055×qC10) + (-85.682×qC12) + (-32.997×qO22) + (-85.129 EHOMO) + (19.724×ELUMO). Statistical value of R2 = 0.8732, rm2 = 0.7935, r2-r02/r2 = 0.0118, PRESS = 1.5727 and Fcalc/Ftable = 2.4067 used as external validation. Atomic net charge showed as the most important descriptor to predict activity and design new molecule. Following QSAR analysis, Lipinski rules was applied to filter the design compound due to physicochemical properties and resulted that all filtered compounds did not violate the rules. Docking analysis was conducted to determine interaction between proposed compounds and EGFR protein. Critical hydrogen bond was found in Met769 residue suggesting that proposed compounds could be used to inhibit EGFR protein.

Study on Anti-Tumor Activity of Novel 3-Substituted 4 Anilino-Coumarin Derivatives Using Quantitative Structure-Activity Relationship (QSAR)

2019 ◽  
Vol 948 ◽  
pp. 101-108 ◽  
Author(s):  
Daratu E.K. Putri ◽  
Harno Dwi Pranowo ◽  
Winarto Haryadi
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Linear Regression Method ◽  
Coumarin Derivatives ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Anti Tumor Activity

Study on anti breast cancer activity of 3-substituted 4-anilino coumarin derivatives by using quantitative structure-activity relationship (QSAR) has been performed. The structures and the activity data were literatured from Guoshun et al. experiment. The molecular and electronic molecule properties were obtained from DFT/BPV86 6-31G method calculation after was through methods validation. The QSAR analysis were shown by Multi Linear Regression method (MLR). The best model of obtained for 3-substituted 4-anilino coumarin derivatives is: Log IC50 = 5.905 + (0.936 x qC1) + (-8.225 x qC8) + (-0.582 x qC13) + (11.273 x qC15) + (0.869 x ∆E) ; n = 26; r2= 0.704; Fcal/Ftab = 2.462; SEE = 0.184.

scholarly journals Quantitative Structure-Activity Relationship Studies for Potential Rho-Associated Protein Kinase Inhibitors

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Giovanna Cardoso Gajo ◽  
Tamiris Maria de Assis ◽  
Letícia Cristina Assis ◽  
Teodorico Castro Ramalho ◽  
Elaine Fontes Ferreira da Cunha
Keyword(s):  
Protein Kinase ◽  
Kinase Inhibitors ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Protein Kinase Inhibitors ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity ◽  
New Compounds

A series of pyridylthiazole derivatives developed by Lawrence et al. as Rho-associated protein kinase inhibitors were subjected to four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. The models were generated applying genetic algorithm (GA) optimization combined with partial least squares (PLS) regression. The best model presented validation values ofr2=0.773,qCV2=0.672,rpred2=0.503,Δrm2=0.197,rm test2⁡⁡=0.520,rY-rand2=0.19, andRp2=0.590. Furthermore, analyzing the descriptors it was possible to propose new compounds that predicted higher inhibitory concentration values than the most active compound of the series.

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