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

[21] Prediction of antibacterial activity of pleuromutilin derivatives by genetic algorithm–multiple linear regression (GA–MLR) [J].

Mohsen Dolatabadi ;

Mehdi Nekoei ;

Alireza Banaei .

Monatshefte für Chemie - Chemical Monthly, 2010, 141 :577-588

[22] QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions [J].

ESLAM POURBASHEER ;

SAADAT VAHDANI ;

REZA AALIZADEH ;

ALIREZA BANAEI ;

MOHAMMAD REZA GANJALI .

Journal of Chemical Sciences, 2015, 127 :1243-1251

[23] Prediction of antileukemia activity of berbamine derivatives by genetic algorithm-multiple linear regression [J].

Nekoei, Mehdi ;

Salimi, Mahmoud ;

Dolatabadi, Mohsen ;

Mohammadhosseini, Majid .

MONATSHEFTE FUR CHEMIE, 2011, 142 (09) :943-948

[24] QSAR Study of Malonyl-CoA Decarboxylase Inhibitors Using GA-MLR and a New Strategy of Consensus Modeling [J].

Li, Jiazhong ;

Lei, Reilei ;

Liu, Huanxiang ;

Li, Shuyan ;

Yao, Xiaojun ;

Liu, Mancang ;

Gramatica, Paola .

JOURNAL OF COMPUTATIONAL CHEMISTRY, 2008, 29 (16) :2636-2647

[25] Prediction of PCE of fullerene (C60) derivatives as polymer solar cell acceptors by genetic algorithm-multiple linear regression [J].

Pourbasheer, Eslam ;

Banaei, Alireza ;

Aalizadeh, Reza ;

Ganjali, Mohammad Reza ;

Norouzi, Parviz ;

Shadmanesh, Javad ;

Methenitis, Constantinos .

JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY, 2015, 21 :1058-1067

[26] Application of ab initio theory to QSAR study of 1,4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures [J].

Hemmateenejad, B ;

Safarpour, MA ;

Miri, R ;

Taghavi, F .

JOURNAL OF COMPUTATIONAL CHEMISTRY, 2004, 25 (12) :1495-1503

[27] Prediction of Solubility of Fullerene C60 in Various Organic Solvents by Genetic Algorithm-Multiple Linear Regression [J].

Pourbasheer, Eslam ;

Riahi, Siavash ;

Ganjali, Mohammad Reza ;

Norouzi, Parviz .

FULLERENES NANOTUBES AND CARBON NANOSTRUCTURES, 2011, 19 (07) :585-598

[28] THE PREDICTION OF KOVATS RETENTION INDICES OF ESSENTIAL OILS AT GAS CHROMATOGRAPHY USING GENETIC ALGORITHM-MULTIPLE LINEAR REGRESSION AND SUPPORT VECTOR REGRESSION [J].

Noviandy, Teuku Rizky ;

Maulana, Aga ;

Sasmita, Novi Reandy ;

Suhendra, Rivansyah ;

Irvanizam, Irvanizam ;

Muslem, Muslem ;

Idroes, Ghazi Mauer ;

Yusuf, Muhammad ;

Sofyan, Hizir ;

Abidin, Taufik Fuadi ;

Idroes, Rinaldi .

JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2022, 17 (01) :306-326

[29] IDENTIFICATION OF NEW HIV-1 PROTEASE INHIBITORS BY MULTIPLE LINEAR REGRESSION (MLR) AND PHYSICO-CHEMICAL DESCRIPTORS [J].

Nandan, Kumar ;

Ahmad, Md. Belal ;

Ranjan, Kumar ;

Sah, Baidyanath .

INTERNATIONAL JOURNAL OF PHARMACEUTICAL SCIENCES AND RESEARCH, 2013, 4 (10) :3971-3975

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

Afshin Maleki ;

Hiua Daraei ;

Loghman Alaei ;

Aram Faraji .

Russian Journal of Bioorganic Chemistry, 2014, 40 :61-75

← 1 2 3 4 →