Application of ab initio theory for the prediction of acidity constants of some 1-hydroxy-9,10-anthraquinone derivatives using genetic neural network

Hemmateenejad, Bahram; Safarpour, Mohammad Ali; Taghavi, Fariba

doi:10.1016/s0166-1280(03)00418-4

Cited by 56 publications

(41 citation statements)

References 32 publications

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“…These are aqueous pK a of substituted acetic acids, aniline [31], substituted phenols and benzoic acids [32], and the acidity of some newly synthesized 1-hydroxy-9, 10-anthraquinone derivatives in methanol-water binary solvents with different compositions [11]. The structures and acidity constant data (both experimental and calculated) are represented as supporting information.…”

Section: Acidity Constant Of Different Set Of Moleculesmentioning

confidence: 99%

“…The resulting QSPR model for these types of molecules (Table II) The SED parameters were also used to find a QSPR model for acidity constant in non-aqueous media. To do so, the acidity constants of some 1-hydroxy-9,10-anthraquinone derivatives in binary methanol-water mixed solvents were taken [11] (Table V, in Supporting Information). Previously, we reported linear and nonlinear QSPR models for prediction of the acidity constant of these newly synthesized 9,10-anthraquinone derivatives in different binary mixed solvents of methanol and water [11].…”

Section: Acidity Constant Of Different Set Of Moleculesmentioning

confidence: 99%

“…Currently, although ab initio methods are the state of the art for studying small molecules, semi-empirical calculations appear to be the method of choice when calculating quantum chemical descriptors [8,9]. However, with the advent of modern computational capabilities and the development of fast algorithms, ab initio quantum chemical calculations can be expeditiously applied in current QSAR studies [10][11][12][13]. Very recently, we successfully applied ab initio theory to predict the acidity constant of anthraquinone derivatives [11], to develop QSAR models for calcium channel antagonist activity of dihydropyridine derivatives [12] and to model the blood-brain partitioning of a large set of solutes [13].…”

Section: Introductionmentioning

confidence: 99%

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Substituent electronic descriptors for fast QSAR/QSPR

Hemmateenejad

Sanchooli

2007

Journal of Chemometrics

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Substituent electronic descriptors (SED), calculated by ab initio quantum chemical methods for radical substituents, were proposed as an efficient and simple to use descriptors for use in Quantitative structure-activity/property relationships (QSPR/QSAR) studies. Twenty five SED parameters were calculated for a set of simple substituents using orbital energies, local charges, and dipole moments. Calculation of these parameters for a substituent takes much lower time comparing with that for parent molecule. Different chemical and biological data were analyzed by the SED parameters and it was found that in addition to the simplicity and speed of calculations, models obtained by SED parameters have better or comparable efficiency in relative to existing models.

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Section: Acidity Constant Of Different Set Of Moleculesmentioning

confidence: 99%

Section: Acidity Constant Of Different Set Of Moleculesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Substituent electronic descriptors for fast QSAR/QSPR

Hemmateenejad

Sanchooli

2007

Journal of Chemometrics

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“…The nucleophilicity, n = E HOMO -E LUMO [37], of the studied molecules was calculated and the aqueous phase calculated n values of the studied neutral molecules are depicted in Table 7.…”

Section: Aciditymentioning

confidence: 99%

Quantum chemical studies on tautomerism, isomerism and deprotonation of some 5(6)-substituted benzimidazole-2-thiones

Öğretır¹,

Öztürk²,

Tay³

2007

Arkivoc

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Acidity constants, pK a values, tautomeric and isomeric equilibrium constants, K T and K eq. values, of some 5(6)-substituted benzimidazole-2-thiones and related fixed models, in which the possibility of proton migration is eliminated by replacing the mobile proton with methyl group, were calculated using semi-empirically computed physical and thermodynamic parameters. Full geometry optimization was carried out using semi-empirical AM1, PM3 and PM5 methods. The theoretically calculated acidity constants were compared with the experimental values and a reasonable correlation was observed.

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“…The strength of modeling with layered, feed-forward artificial neural network lies in the flexibility of the distributed soft model defined by the weight of the network. Multilayer feed-forward neural networks trained with a back-propagation learning algorithm have become increasingly popular techniques [19][20][21] . The flexibility of ANN for discovering more complex relationships lead this method to find a wide application in QSAR/QSTR studies, as recently reviewed by Schneider and Wrede 22 .…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariate Image Analysis - Quantitative Structure Toxicity Relationship Study for Modeling the Toxicity of Phenol Derivatives

Garkani-Nejad

Salehfard

2013

J. Chil. Chem. Soc.

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The toxicity of some phenol derivatives over tetrahymena pyriformis were modeled by using multivariate image analysis (MIA) descriptors applied to quantitative structure-toxicity relationship (QSTR) method. MIA descriptors are derived from pixels of two-dimensional (2D) chemical structures that may be built by using any appropriate drawing software. QSTR analysis has been done by using partial least square regression (PLS), correlation ranking-principal component regression (CR-PCR) and correlation ranking-principal component-artificial neural network (CR-PC-ANN) modeling methods. For the ANN models two different weight update functions of Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) were investigated. Finally, obtained results using different linear and nonlinear modeling methods were compared, showing that PLS and CR-PC-ANN-SCG methods present high performance for prediction of the toxicity of phenol derivatives. Also, these results indicated that MIA descriptors may be useful to predict toxicity of phenol derivatives. Squared correlation coefficient (R 2 ) and standard error using the PLS method for the training set were 0.996 and 0.042 and for the test set were 0.996 and 0.048, respectively. Squared correlation coefficient (R 2 ) and standard error using the CR-PC-ANN-SCG method were respectively, 0.999 and 0.002 for the training set, 0.999 and 0.001 for the test set and 0.999 and 0.002 for the validation set.

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Application of ab initio theory for the prediction of acidity constants of some 1-hydroxy-9,10-anthraquinone derivatives using genetic neural network

Cited by 56 publications

References 32 publications

Substituent electronic descriptors for fast QSAR/QSPR

Substituent electronic descriptors for fast QSAR/QSPR

Quantum chemical studies on tautomerism, isomerism and deprotonation of some 5(6)-substituted benzimidazole-2-thiones

Application of Multivariate Image Analysis - Quantitative Structure Toxicity Relationship Study for Modeling the Toxicity of Phenol Derivatives

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