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Hemmateenejad, Bahram; Sharghi, Hashem; Akhond, Morteza; Shamsipur, Mojtaba

doi:10.1023/a:1022982200712

Cited by 22 publications

(7 citation statements)

References 32 publications

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“…21 Hence, selecting the significant and informative PC's is the main problem in all of the PCA-based calibration methods. [22][23][24][25] Different methods have been addressed to select the significant PC's for calibration purposes. The simplest and most common one is a top-down variable selection where the PC's are ranked in the order of decreasing eigenvalues and the PC's with highest eigenvalue is considered as the most significant one and, subsequently, the PC's are introduced into the calibration model.…”

Section: Introductionmentioning

confidence: 99%

“…25 In the other method, which is called correlation ranking, the PC's are ranked by their correlation coefficient with the property and selected by the procedure discussed for eigenvalue ranking. 22,23 Better results are often achieved by this method. Recently, genetic algorithm (GA) has been applied for the selection of the most relevant PC's instead of the older methods.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

Habibi‐Yangjeh

Pourbasheer²,

Danandeh-Jenagharad³

2008

Bulletin of the Korean Chemical Society

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Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC's) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC's, the genetic algorithm was employed for selection of the best set of extracted PC's for PC-MLR and PC-ANN models. The models were generated using fifteen PC's as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and 12.77 ºC, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = 40.7 ºC). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

Habibi‐Yangjeh

Pourbasheer²,

Danandeh-Jenagharad³

2008

Bulletin of the Korean Chemical Society

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“…All QSAR models are built with application of statistical (mathematical) tools correlating properties of molecules with descriptors that represent molecular structure 5. The DHP family, in which nifedipine is the prototype, have been the aim of many QSAR studies 6–12.…”

Section: Introductionmentioning

confidence: 99%

SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines

Veselinović

Milosavljević

Toropov

et al. 2012

Archiv der Pharmazie

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The activity of 72 1,4-dihydropyridines as calcium channel antagonists was examined. The simplified molecular input-line entry system (SMILES) was used as representation of the molecular structure of the calcium channel antagonists. Quantitative structure-activity relationships (QSARs) were developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the training and test sets. Using the Monte Carlo method, the CORAL software generated the optimal descriptors for one-variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The obtained results reveal good predictive potential of the applied approach: The correlation coefficients (r(2) ) for the test sets of the four random splits are 0.9571, 0.9644, 0.9836, and 0.9444.

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“…At the present time, quantitative structure-property relationships (QSPR) are increasingly employed in the prediction of chemical and physical properties of different types of molecules [1][2][3][4][5][6][7][8]. The main task of QSPR is to obtain a reliable model for the prediction of properties/ behaviors of new chemical substances and analytical systems.…”

Section: Introductionmentioning

confidence: 99%

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

et al. 2009

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Abstract:The half-wave potential (E 1/2 ) is an important electrochemical property of organic compounds. In this work, a quantitative structureproperty relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E 1/2 ) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R 2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R 2 =0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced . This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E 1/2 , and can be useful in predicting the E 1/2 of other compounds. Warsaw and Springer-Verlag Berlin Heidelberg. © Versita

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Cited by 22 publications

References 32 publications

Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

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