scholarly journals Predicting Flash Point of Organosilicon Compounds Using Quantitative Structure Activity Relationship Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chen-Peng Chen ◽  
Chan-Cheng Chen ◽  
Hsu-Fang Chen
Keyword(s):  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Organosilicon Compounds ◽  
Quantitative Structure ◽  
Predictive Error ◽  
Structure Activity ◽  
Flammable Liquids ◽  
Fitting Error

The flash point (FP) of a compound is the primary property used in the assessment of fire hazards for flammable liquids and is amongst the crucial information that people handling flammable liquids must possess as far as industrial safety is concerned. In this work, the FPs of 236 organosilicon compounds were collected and used to construct a quantitative structure activity relationship (QSAR) model for predicting their FPs. The CODESSA PRO software was adopted to calculate the required molecular descriptors, and 350 molecular descriptors were developed for each compound. A modified stepwise regression algorithm was applied to choose descriptors that were highly correlated with the FP of organosilicon compounds. The proposed model was a linear regression model consisting of six descriptors. This 6-descriptor model gave anR2value of 0.9174,QLOO2value of 0.9106, andQ2value of 0.8989. The average fitting error and the average predictive error were found to be of 10.34 K and 11.22 K, respectively, and the average fitting error in percentage and the average predictive error in percentage were found to be of 3.30 and 3.60%, respectively. Compared with the known reproducibility of FP measurement using standard test method, these predicted results were of a satisfactory precision.

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AND MOLECULAR DOCKING STUDIES OF HYDROXAMIC ACID DERIVATIVES AS NOVEL CLASS INHIBITORS AGAINST HELICOBACTER PYLORI UREASE

2019 ◽  
Vol 5 (5) ◽  
pp. 0482-0493
Author(s):  
Ibrahim Tijjani Ibrahim ◽  
Adamu Uzairu ◽  
Balarabe Sagagi
Keyword(s):  
Molecular Docking ◽  
Hydroxamic Acid ◽  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Antiulcer Activity ◽  
Quantitative Structure ◽  
Determination Coefficient ◽  
Structure Activity

In order to develop quantitative structure-activity relationship (QSAR), for predicting antiulcer activity of hydroxamic acid analogues use as dataset and their antiulcer activity were obtained from the literature. Density Functional Theory (DFT) using B3LYP/6-31G* quantum chemical calculation method was used to find the optimized geometry of the studied compounds. Eight types of molecular descriptors were used to find out the relation between antipeptic ulcer (APU) activity and structural properties. Relevant molecular descriptors were selected by Genetic Function Algorithms (GFA). The best model obtained was given a distinct validated, good and robust statistical parameters which include; square correlation coefficient R2 value of (0.9989), adjusted determination coefficient, R2adj value of (0.9984), Leave one out cross validation determination coefficient Q2 value of (0.9948) and external validation as predicted determination coefficient R2 value of(0.8409). Molecular docking analysis find out that, the best lead-compound with the higher negative value score of (-8.5 kcal/mol) were formed hydrophobic interaction and H-bonding with amino acid residue between the inhibitors compounds with their respective receptor.

scholarly journals Comparison of Quantitative Structure-Activity Relationship Model Performances on Carboquinone Derivatives

2009 ◽  
Vol 9 ◽  
pp. 1148-1166 ◽  
Author(s):  
Sorana D. Bolboaca ◽  
Lorentz Jäntschi
Keyword(s):  
Goodness Of Fit ◽  
Molecular Descriptors ◽  
Information Criterion ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Information Criteria ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models

Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike?s information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steiger's Z test, and Akaike's weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steiger's Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaike's weights.

Predicting human skin absorption of chemicals: development of a novel quantitative structure activity relationship

2007 ◽  
Vol 23 (1) ◽  
pp. 39-45 ◽  
Author(s):  
Wen Luo ◽  
Sarah Medrek ◽  
Jatin Misra ◽  
Gerhard J Nohynek
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Molecular Size ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Skin Absorption ◽  
Quantitative Structure ◽  
Efficacy Evaluation ◽  
Structure Activity

The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental Kow values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with Kp (R2 = 0.8225): logKow, ×0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations. Toxicology and Industrial Health 2007; 23: 39—45.

scholarly journals Quantitative Structure–activity Relationship Studies of Flavonoids Substituted as Anticancer Agents Activity against the Growth of the Hepatic Cancer Cell lines HepG2

2017 ◽  
Vol 9 (2) ◽  
pp. 1 ◽  
Author(s):  
Wisam A. Radhi ◽  
Sadiq M. H. Ismael ◽  
Jasim M. Al-Shawi ◽  
Kawkab A. Hussein
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Significant Role ◽  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Hepatic Cancer ◽  
Quantitative Structure ◽  
Structure Activity

Quantitative Structure–Activity Relationship (QSAR) models, based on molecular descriptors, derived from molecular structures, have been used for the prediction for computed the Hepatic Cancer Cell lines HepG2 of flavonoids substituted. QSAR model including some molecular descriptors, regression quality indicates that these descriptors provide valuable information and have significant role in the assessment of the cytotoxicity of compounds under study. Four QSAR equations, for the prediction of cytotoxicity, have been drawn up by using the multiple regression technique, (Eqs 1-4) with the values of R2 ranged from 0.767-0.797, Q2 ranged from 0.765-0.796 and the values of S ranged from 7.051-7.391, while the values of F ranged from 9.328-10.354. The results have shown excellent model by Eq 4. with high R2,F and minimum S by using eight  parameters [Gm, nO, nH, nCIC, nBM, nAB, D.M and Ku], and have found and indicated that these parameters have significant role in determining the properties of cytotoxicity. This result encourages the application of QSAR to a wider selection of compounds properties.

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