scholarly journals A Benchmark Dataset Comprising Partition and Distribution Coefficients of Linear Peptides

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Matthew N. Davies ◽  
Darren R. Flower
Keyword(s):  
Organic Solvent ◽  
Small Molecules ◽  
Partition Coefficients ◽  
Dominant Role ◽  
Quantitative Structure Activity Relationship ◽  
Chemical Substance ◽  
Distribution Coefficients ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity

Peptides have a dominant role in biology; yet the study of their physical properties is at best sporadic. Peptide quantitative structure-activity relationship (QSAR) lags far behind the QSAR analysis of drug-like organic small molecules. Traditionally, QSAR has focussed on experimentally determined partition coefficients as the main descriptor of hydrophobicity. A partition coefficient () is the ratio between the concentrations of an uncharged chemical substance in two immiscible phases: most typically water and an organic solvent, usually 1-octanol. A distribution coefficient () is the equivalent ratio for charged molecules. We report here a compilation of partition and distribution coefficients for linear peptides compiled from literature reports, suitable for the development and benchmarking of peptide and prediction algorithms.

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.

Biological Activity, Physical Properties, and Toxicity

2021 ◽  
Vol 6 (3) ◽  
pp. 25-34
Author(s):  
Ranita Pal ◽  
Pratim Kumar Chattaraj
Keyword(s):  
Molecular Structure ◽  
Biological Activity ◽  
Molecular Biology ◽  
Drug Design ◽  
Physical Properties ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Properties ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity

In the current pandemic-stricken world, quantitative structure-activity relationship (QSAR) analysis has become a necessity in the domain of molecular biology and drug design, realizing that it helps estimate properties and activities of a compound, without actually having to spend time and resources to synthesize it in the laboratory. Correlating the molecular structure of a compound with its activity depends on the choice of the descriptors, which becomes a difficult and confusing task when we have so many to choose from. In this mini-review, the authors delineate the importance of very simple and easy to compute descriptors in estimating various molecular properties/toxicity.

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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