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Iranian Journal of Chemical Engineering، جلد ۱۶، شماره ۱، صفحات ۸۳-۱۰۰
عنوان فارسی Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models
چکیده فارسی مقاله Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model.
کلیدواژه‌های فارسی مقاله organic compound،، solubility،، artificial neural network،، group contribution،، support vector machine،
عنوان انگلیسی Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models
چکیده انگلیسی مقاله Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model.
کلیدواژه‌های انگلیسی مقاله organic compound, solubility, artificial neural network, group contribution, support vector machine
نویسندگان مقاله A. Yousefi |
Faculty of Chemical Engineering Babol Noshiravani University of Tehnology

k. movagharnejad |


نشانی اینترنتی http://www.ijche.com/article_84431_e9921b3bd541ca2dcd2651afb4081ebb.pdf
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