Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water

Habibi‐Yangjeh, Aziz; Pourbasheer, Eslam; Danandeh-Jenagharad, Mohammad

doi:10.1007/s00706-008-0049-7

Cited by 48 publications

(26 citation statements)

References 45 publications

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“…Insofar as artificial neural networks (ANN) utilize intrinsic mechanisms of input data classification according to specified criteria and without them, such mathematical models are frequently used in QSPR methods to distinguish most statistically significant descriptors [157,159,160,[163][164][165]. Support vector machine (SVM) [157], recursive ANNs (RecNN) [192,193], radial basis ANNs [161], and back propagation networks (BPG) [175] are involved here.…”

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

“…This procedure has become very popular for prediction of many other physicochemical parameters of compounds and their biological activity [150,[176][177][178][179][180][181][182][183][184][185][186][187][188]. Extensive use of QSPR in solving various problems of physical organic chemistry is favored by development of computational methods and instruments and statistical processing procedures such as principal component analysis (PCA) [154,[163][164][165]185], independent component analysis (ICA) [189], partial least squares (PLS) [153,154,156,157,168,174] in combination with iterative variable elimination (IVE-PLS) [156], multilinear regression (MLR) [155,157,159,160,175,187], and variable importance in projection (VIP) [190,191].…”

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

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Modern methods for estimation of ionization constants of organic compounds in solution

Zevatskii

Samoilov²

2011

Russ J Org Chem

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The review considers main up-to-date approaches to the calculation of ionization constants of organic compounds in solutions. Methods based on quantum-chemical calculations, continuum solvent models, cluster models, and quantitative structure-property relationships are discussed. The results of calculations of pK a values by different methods, obtained in the past decade, are compared. REVIEW ACID IONIZATION CONSTANTS AND THEIR SIGNIFICANCEA quantitative parameter of acid-base properties of an HA molecule is the equilibrium constant for proton transfer (K a ) from acid HA to solvent molecule M with formation of lyonium ion and acid anion [1-3] (if an acid is an onium ion, neutral base is formed).

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Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

Modern methods for estimation of ionization constants of organic compounds in solution

Zevatskii

Samoilov²

2011

Russ J Org Chem

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“…Replacing a non-linear modeling instead of linear regression, led to a new technique for QSAR/QSPR, called PC-GA-ANN, which was proposed by Hemmateenejad and coworkers [191,192] and has been utilized in some other articles [189,193].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

122

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“…Hence, computational tools could be useful to predict inhibitory activities of inhibitors in an easy and fast manner. One of the wellestablished and widely used computational techniques in drug design is the quantitative structure-activity relationship (QSAR) method [22][23][24][25][26][27][28], in which the biological profiles of molecules can be derived based on chemical structural information [29][30][31][32][33][34]. Further improvements and developments of this method have led to the generation of the three-dimensional quantitative structure-activity relationship (3D-QSAR) method [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies

Aalizadeh

Pourbasheer

2015

Mol Divers

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In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf[Formula: see text] inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results [Formula: see text], which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values [Formula: see text] based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained [Formula: see text]. The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf[Formula: see text] kinase.

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Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water

Cited by 48 publications

References 45 publications

Modern methods for estimation of ionization constants of organic compounds in solution

Modern methods for estimation of ionization constants of organic compounds in solution

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies

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