QSAR study of ACK1 inhibitors by genetic algorithm-multiple linear regression (GA-MLR)

被引：15

作者：

Pourbasheer, Eslam ^{[1
,2
]}

Aalizadeh, Reza ^{[3
]}

Ganjali, Mohammad Reza ^{[4
]}

Norouzi, Parviz ^{[4
]}

Shadmanesh, Javad ^{[3
]}

机构：

[1] Payame Noor Univ, Dept Chem, Tehran, Iran

[2] Islamic Azad Univ, Ardabil Branch, Young Researchers & Elite Club, Ardebil, Iran

[3] Natl & Kapodistrian Univ Athens, Dept Chem, Athens 15771, Greece

[4] Univ Tehran, Fac Chem, Ctr Excellence Electrochem, Tehran, Iran

来源：

JOURNAL OF SAUDI CHEMICAL SOCIETY | 2014年 / 18卷 / 05期

关键词：

QSAR; Genetic algorithm; Hierarchical clustering; Multiple linear regressions; ACK1; ACTIVATED CDC42-ASSOCIATED KINASE; TYROSINE KINASE; STRUCTURE/RESPONSE CORRELATIONS; SIMILARITY/DIVERSITY ANALYSIS; TROPOSPHERIC DEGRADATION; GETAWAY DESCRIPTORS; ANDROGEN RECEPTOR; NEURAL-NETWORK; TEST SETS; PREDICTION;

D O I：

10.1016/j.jscs.2014.01.010

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In this work, a quantitative structure-activity relationship (QSAR) model was used to predict the ACK1 inhibitory activities. A data set of 37 compounds with known ACK1 inhibitory activities was used. The data set was divided into two subsets of training and test sets, based on hierarchical clustering technique. Genetic algorithm was applied to select the respective variables to build the model in the next step. Multiple linear regressions (MLR) were employed to give the QSAR model. The squared cross-validated correlation coefficient for leave-one-out (Q(LOO)(2)) of 0.712 and squared correlation coefficient (R-train(2)) of 0.806 were obtained for the training set compounds by GA-MLR model. The given model performed a good stability and predictability when it was verified by internal and external validation. The predicted results from this study can lead to design of better and potent ACK1 inhibitors. (C) 2014 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

引用

页码：681 / 688

页数：8

共 36 条

[31] Quantitative structure-property relationship models to predict thermodynamic properties of some mono and polycyclic aromatic hydrocarbons using genetic algorithm-multiple linear regression [J].

Dialamehpour, Fatemeh ;

Shafiei, Fatemeh .

JOURNAL OF THE CHINESE CHEMICAL SOCIETY, 2020, 67 (06) :969-982

[32] Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict K d of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors [J].

Maleki, Afshin ;

Daraei, Hiua ;

Alaei, Loghman ;

Faraji, Aram .

RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY, 2014, 40 (01) :61-75

[33] QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression [J].

Darnag, Rachid ;

Minaoui, Brahim ;

Fakir, Mohamed .

ARABIAN JOURNAL OF CHEMISTRY, 2017, 10 :S600-S608

[34] QSAR study of the non-peptidic inhibitors of procollagen C-proteinase based on Multiple linear regression, principle component regression, and partial least squares [J].

Khazaei, Ardeshir ;

Sarmasti, Negin ;

Seyf, Jaber Yousefi ;

Rostami, Zahra ;

Zolfigol, Mohammad Ali .

ARABIAN JOURNAL OF CHEMISTRY, 2017, 10 (06) :801-810

[35] QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5-b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors [J].

Jafari, Mahdi ;

Momeni Isfahani, Tahereh ;

Shafiei, Fatemeh ;

Senejani, Masumeh Abdoli .

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, 2024, 124 (01)

[36] Quantitative structure-activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression [J].

Cong, Yong ;

Li, Bing-ke ;

Yang, Xue-gang ;

Xue, Ying ;

Chen, Yu-zong ;

Zeng, Yi .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2013, 127 :35-42

← 1 2 3 4 →