Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network

Deeb, Omar; Jawabreh, Mohammad

doi:10.4236/aces.2012.21010

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…When compared with the traditional statistical methods, the flexibility of ANN enables it to discover more complex relationships in experimental data. 17…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides

Bouakkadia¹,

Kertiou²,

Amiri

et al. 2021

J Serb Chem Soc

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The partitioning tendency of pesticides, in these study herbicides in particular, into different environmental compartments depends mainly of the physic-chemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure determining aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, every time associated with genetic algorithm (GA) selection of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series 80 herbicides. The values of log S of the studied compounds were well correlated with de descriptors. Considering the pertinent descriptors, a Pearson Correlation Squared (R2) coefficient of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

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“…When compared with the traditional statistical methods, the flexibility of ANN enables it to discover more complex relationships in experimental data. 17…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides

Bouakkadia¹,

Kertiou²,

Amiri

et al. 2021

J Serb Chem Soc

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“…The QSAR model was generated previously by Multiple Linear Regression (MLR) Backward method by using SPSS Release 19.0.0 package (IBM, 2010 Because of the linear analysis method produced a model that does not pass the validation test, then it proceeds with the analysis of the non-linear form of artificial neural networks (ANN) as performed by Deeb and Jawabreh (2012) using MATLAB package. On ANN analysis, examination of data outliers in advance.…”

Section: Model Developmentmentioning

confidence: 99%

“…Before analyzing with ANN, the data were checked for the outliers of the twentieth data that would not use by principle component analysis (PCA) as done by Deeb and Drabh (2010), Deeb and Jawabreh (2012). For example, the result of data analysis for outliers in AM1 data showed in Figure 2.…”

Section: Model Validationmentioning

confidence: 99%

Design of Low Cytotoxicity Diarylaniline Derivatives Based on Qsar Results: An Application of Artificial Neural Network Modelling

Arief

Armunanto

Setiaji

et al. 2016

Molekul

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Study on cytotoxicity of diarylaniline derivatives by using quantitative structure-activity relationship (QSAR) has been done. The structures and cytotoxicities of diarylaniline derivatives were obtained from the literature. Calculation of molecular and electronic parameters was conducted using Austin Model 1 (AM1), Parameterized Model 3 (PM3), Hartree-Fock (HF), and density functional theory (DFT) methods. Artificial neural networks (ANN) analysis used to produce the best equation with configuration of input data-hidden node-output data = 5-8-1, value of r 2 = 0.913; PRESS = 0.069. The best equation used to design and predict new diarylaniline derivatives. The result shows that compound N1-(4′-Cyanophenyl)-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy)-4-dimethylether benzene-1,2-diamine) is the best-proposed compound with cytotoxicity value (CC 50 ) of 93.037 μM.Keywords : diarylaniline, cytotoxicity, QSAR, ANN ABSTRAK Kajian terhadap sitotoksisitas turunan diarilanilina menggunakan hubungan kuantitatif strukturaktifitas (HKSA) telah dilakukan. Struktur dan sitotoksisitas dari turunan diarilanilina diperoleh dari literatur. Perhitungan parameter molekuler dan elektronik dilakukan dengan metode Austin Model 1 (AM1), Parameterized Model 3 (PM3), Hartree-Fock (HF), dan density functional theory (DFT). Analisis jaringan syaraf tiruan (JST) digunakan untuk menghasilkan persamaan terbaik dengan konfigurasi dari input data-hidden node-output data = 5-8-1, nilai r 2 = 0,913; PRESS = 0,069. Persamaan terbaik tersebut kemudian digunakan untuk merancang dan memprediksi senyawa-senyawa turunan diarilanilina yang baru. Hasil yang diperoleh menunjukkan bahwa senyawa N 1 -(4′-sianofenil)-5-(4″-sianofinil-2″,6″-dimetil-fenoksi)-4-dimetileter benzena-1,2-diamina) adalah senyawa usulan terbaik dengan nilai sitotoksisitas (CC 50 ) sebesar 93,037 μM.

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“…Studies to discover anti-HIV compounds using QSAR methods also developed, i.e. QSAR studies on anti-HIV activity of derivatives of phenyl ethyl thiourea [9], peptide [10], cyanoguanidine [11], and diaryl pirimidine [12]. Based on the data of effective concentration (EC 50 , nM) of DAAN derivatives reported by Sun et al [7] ( Table 1), we have predicted a new compounds of diarylaniline derivative using QSAR procedure.…”

Section: Introductionmentioning

confidence: 99%

Study on Anti-Hiv Activity of Diarylaniline Derivatives Using Quantitative Structure-Activity Relationship (Qsar)

Arief¹,

Armunanto²,

Setiaji³

2013

Indones. J. Chem.

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Study on anti-HIV activity of diarylaniline derivative compounds by using quantitative structure-activity relationship (QSAR) -(4′-Cyanophenyl)-5-(4″-cyanovinyl-2″,6″-dimethyl-phenoxy)-4-hydroxyethylbenzene-1,2-diamine). Keywords: diarylaniline; anti-HIV; QSAR ABSTRAK Telah dilakukan kajian terhadap aktivitas anti-HIV dari senyawa turunan diaril anilina menggunakan model hubungan kuantitatif struktur-aktivitas (HKSA). Struktur senyawa dan aktivitas anti-HIV

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Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network

Cited by 4 publications

References 27 publications

Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides

Use of GA-ANN and GA-SVM for a QSPR study on the aqueous solubility of pesticides

Design of Low Cytotoxicity Diarylaniline Derivatives Based on Qsar Results: An Application of Artificial Neural Network Modelling

Study on Anti-Hiv Activity of Diarylaniline Derivatives Using Quantitative Structure-Activity Relationship (Qsar)

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