• Bulletin of the Korean Chemical Society
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• 제28권9호
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• pp.1477-1484
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• 2007

Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.

• Bulletin of the Korean Chemical Society
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• 제15권2호
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• pp.106-112
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• 1994

Six physical properties (molecular weight, heat capacity, side chain weight, side chain volume, standard entropy and partial molar volume) of amino acids, peptides and their derivatives were examined by molecular modeling techniques. The molecular connectivity index, Wiener distance index and ad hoc descriptor are employed as structural parameters to encode information about branching, size, cyclization, unsaturation, heteroatom content and polarizability. This paper examines the correlation of the molecular modeling techique's parameters and the physicochemical properties of amino acids and their derivatives. As a result, calculated values were in agreement with experimental data in the above six physical properties of amino acids, peptides and their derivatives and the molecular connectivity index was superior to the other indices in fitting the calculated data.