Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network

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

doi:10.1007/s00706-008-0951-z

Cited by 36 publications

(14 citation statements)

References 36 publications

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“…The labelling yields obtained for the halopyridine analogues are not directly correlated to their respective pK a values—or the ratio pyridinium/pyridine—although the −I effect of the bromine or iodine atom and its position on the heteroaromatic ring both influence the basicity of the lone pair bearing N atom . The labelling yields reflect the general theory claiming that in the pyridine ring substituted with a halogen atom at the 2‐position or 4‐position, those positions are activated for initial nucleophilic attack, whereas the 3‐position is less favourable .…”

Section: Resultsmentioning

confidence: 91%

New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

Eersels

Mertens

Herscheid

2012

Labelled Comp Radiopharmac

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The Cu + method, originally developed for aromatic compounds, has also been applied for radioiodination of pyridine rings. The aim of this paper is a more fundamental approach of the different parameters ruling the Cu + -assisted radioiodination of the regiomers of pyridine.A mechanistic model is represented on the basis of the mutual complex-formation properties of pyridine for Cu + and Sn ++ . It is proven that a part of the Cu + catalyst, required for optimal labelling, is involved in a Halo-PyrN:Cu + -complex resulting in a lower labelling yield. In case of 3-Br-pyridine and 4-Br-pyridine, the Sn ++ present in excess versus Cu + in the general reaction conditions, can displace Cu + from its pyridine complex rendering it available as catalyst, allowing a successful labelling.A 2-Br-pyridine, with the halogen atom in ortho position of the N atom, is an exception to that rule: the Cu + ions reversibly bound in the Br-Pyr-N:Cu + -complex are also involved in an intramolecular transfer to the ortho C-Br bond resulting in an effective molarity exceeding the bulk concentration and a high labelling yield.Radioiodination of a 2-halo-pyridine analogue is preferentially performed in the absence of SnSO 4 , using only gentisic acid as reducing agent. Materials and methods ReagentsAll reagents and solvents were obtained from commercial suppliers and were HPLC grade or analytical grade and used as such.Nitrogen was 5.0-grade and purchased from Hoekloos, The Netherlands.Radioiodide (Na 123 I, no carrier added; specific activity of 8695 GBq/mmol) was supplied by Covidien, The Netherlands. Radioiodide recovery for labellingThe supplied Na 123 I solution (600 MBq, 60 MBq/ml, pH~7) was concentrated and recovered in a 10 À2 mol.L À1 NaOH solution, using the earlier described Values are represented as mean-values, n = 5 (mean absolute deviation: AE1.5%). Reaction mixture; 5.1 mmol precursor, 3.2 Â 10 À5 mol gentisic acid, 1.9 Â 10 À5 mol citric acid, X mol SnSO 4 and 3.9 Â 10 À7 mol CuSO 4 .5H 2 O. Reaction conditions; 40 min at 110 C. J. L. H. Eersels et al.

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Section: Resultsmentioning

confidence: 91%

New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

Eersels

Mertens

Herscheid

2012

Labelled Comp Radiopharmac

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“…For this purpose, as described in the Experimental section, the data set was randomly divided into three groups: training, validation, and prediction sets consisting of 20, 7, and 7 data points, respectively. The training and validation sets were used for the model generation, and the prediction set was used for the evaluation of the generated model [34][35][36][37][38]. There are no rigorous theoretical principles for choosing the proper network topology, so different structures were tested to obtain the optimal hidden neurons and training cycles [29].…”

Section: Resultsmentioning

confidence: 99%

“…These give ANNs an advantage over traditional fitting methods for some chemical application. For these reasons in recent years, ANNs have been used in a wide variety of chemical problems [30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Solvent effects on kinetics of an aromatic nucleophilic substitution reaction in mixtures of an ionic liquid with molecular solvents and prediction using artificial neural networks

Habibi‐Yangjeh

Jafari-Tarzanag

Banaei

2008

Int J of Chemical Kinetics

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Kinetics of the reaction between 1-chloro-2,4-dinitrobenzene and aniline was studied in mixtures of [EtSO 4 ]) with methanol, chloroform, and dimethylsulfoxide at 25 • C. Single-parameter correlations of log k A versus normalized polarity parameter (E N T ), hydrogen-bond acceptor basicity (β), hydrogen-bond donor acidity (α), and dipolarity/polarizability (π * ) of media do not give acceptable results. Multiparameter linear regression (MLR) of log k A versus the solvatochromic parameters demonstrates that the reaction rate constant increases with E N T , π * , and β and decreases with α parameter. To predict accurately solvent effects on the rate constant, optimized artificial neural network with three inputs (including α, π * , and β parameters) was applied for prediction of the log k A values in the prediction set. It was found that properly selected and trained neural network could fairly represent the dependence of the reaction rate constant on solvatochromic parameters. Mean percent deviation of 5.023 for the prediction set by the MLR model should be compared with the value of 0.343 by the artificial neural network model. These improvements are due to the fact that the reaction rate constant shows nonlinear correlations with the solvatochromic parameters. C

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“…Another study compared various publicly available methods using over 2000 experimental data points [15]. Also the more difficult to predict melting points were reported [46], as well as pKa neural network methods [47]. Very recently a neural network based approach was reported for the calculation of molecular energies, claiming less than 3 kJ/mole (0.6 kcal/mole) difference between DFT results and the neural network method [48].…”

Section: Qspr Methods Using Molecular Descriptorsmentioning

confidence: 99%

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Meier

2019

Applied Sciences

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Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

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Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network

Cited by 36 publications

References 36 publications

New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

Solvent effects on kinetics of an aromatic nucleophilic substitution reaction in mixtures of an ionic liquid with molecular solvents and prediction using artificial neural networks

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

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Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network

Cited by 36 publications

References 36 publications

New insights into the Cu+‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

New insights into the Cu+‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

Solvent effects on kinetics of an aromatic nucleophilic substitution reaction in mixtures of an ionic liquid with molecular solvents and prediction using artificial neural networks

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

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Product

Resources

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New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues

New insights into the Cu⁺‐assisted nucleophilic radioiodination of bromopyridine and iodopyridine analogues