Quantitative Structure-Toxicity Relationship for Predicting Acute Toxicity of Alkylbenzenes

2014 ◽  
Vol 665 ◽  
pp. 571-574 ◽  
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu
Keyword(s):  
Linear Regression ◽  
Acute Toxicity ◽  
Multiple Linear Regression ◽  
Health Problem ◽  
Molecular Descriptors ◽  
Determinants Of Health ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Acute toxicity is an important toxicological endpoint which poses a great concern being the major determinants of health problem, a quantitative structure toxicity relationship (QSTR) study was performed for the prediction of the acute toxicity of alkylbenzenes. The molecular descriptors of alkylbenzenes have been calculated with semi-empirical AM1 and E-dragon methods, and QSTR model for mice via the oral LD50 model of alkylbenzenes was developed using multiple linear regression (MLR) analysis.

QSTR Study for the Carcinogenicity of Alkylbenzenes

2014 ◽  
Vol 665 ◽  
pp. 567-570
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Health Problem ◽  
Molecular Descriptors ◽  
Determinants Of Health ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of health problem, a quantitative structure toxicity relationship (QSTR) study was performed for the prediction of the carcinogenicity of alkylbenzenes. The molecular descriptors of alkylbenzenes have been calculated with semi-empirical AM1 and E-dragon methods, and QSTR model for mice carcinogenic model of alkylbenzenes were developed using multiple linear regression (MLR) analysis.

Predicting Carcinogenicity of Anilines by Quantitative Structure-Toxicity Relationship

2014 ◽  
Vol 665 ◽  
pp. 559-562 ◽  
Author(s):  
Zhi Xiang Zhou ◽  
Yang Hua Liu ◽  
Xiao Long Zhang
Keyword(s):  
Molecular Structure ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Test Set ◽  
Proposed Model ◽  
Structure Toxicity Relationship ◽  
Semi Empirical ◽  
Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of cancers and tumours. Anilines possess such toxic properties as they can form various electrophilic intermediates and adducts with biological systems. In the present work, the molecular descriptors of anilines have been calculated with semi-empirical AM1 and E-dragon methods, and a quantitative structure–toxicity relationships (QSTR) model for carcinogenic potency (pTD50) model of anilines was developed with multiple linear regression (MLR) analysis. The validation results through the test set indicate that the proposed model is robust and satisfactory. The QSTR study suggests that the molecular structure and the electronegativity of chemicals are closely related to the Carcinogenicity.

scholarly journals Prediction of the retention of β-diketonato complexes in TLC systems on silica gel by quantitative structure-retention relationships

2010 ◽  
Vol 75 (4) ◽  
pp. 513-521 ◽  
Author(s):  
Rada Baosic ◽  
Ana Radojevic ◽  
Zivoslav Tesic
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Area ◽  
Molecular Orbital ◽  
Silica Gel ◽  
Molecular Descriptors ◽  
Quantitative Structure ◽  
Hydrophilic Lipophilic Balance ◽  
Structure Retention ◽  
Retention Parameters

Quantitative structure-retention relationships for a series of 30 mixed ?-diketonato complexes of cobalt(III), chromium(III) and ruthenium(III) were derived by multiple linear regression analyses using molecular descriptors obtained by quantum chemical calculations. The retention parameters were obtained by thin layer chromatography on silica gel using mono and two-component solvent systems. The molecular descriptors included in the multiple linear regression analysis were molecular weight, molecular volume, surface area, hydrophilic-lipophilic balance, percent hydrophilic surface area, dipole moment, polarizability, refractivity, energy of the highest occupied molecular orbital and energy of the lowest unoccupied molecular orbital. High agreement between the experimental and predicted retention parameters was obtained when polarizability and the hydrophilic-lipophilic balance were used as the molecular descriptors. Comparison of the models with those established on polyacrylonitrile showed that the structure of the sorbent is responsible for the chromatographic behaviour of the same compounds. The presented models can be used for the prediction of the retention of new solutes in screening chromatographic systems.

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

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