Prediction of boiling points of organic compounds from molecular descriptors by using backpropagation neural network
The design and optimisation of industrial process require the knowledge of thermophysical properties. Available data banks can provide this information. However in specific cases, such as those related to drug activity or enviromental impact assessment, data are scarce and difficult or expensive to obtain experimentally. To overcome this lack of ready information, several thermodynamic models and correlations have been developed for a wide range of conditions. Among these models, the methods based on quantitative structure property relationships (QSPR) are promising. The basic concept of QSPR is to relate the structure of a compound with the property of interest. The compound’s structure is expressed in terms of molecular descriptors that characterise a given molecular feature. Molecular descriptors, such as the connectivity indices and the corresponding valence connectivity indices, that encode features such as size, branching, unsaturation, heteroatom content and cyclicity [1,2] are useful. For example, the first order connectivity index was used in 1982 to correlated the solubility of hydrocarbons in water . The connectivity indices are based on local molecular properties and are bond-additive quantities so that in bonds of different kinds make different contribution to the overall molecular descriptors. The key step is to build the structure property relationship.
KeywordsBoiling Point Molecular Descriptor Hide Unit Connectivity Index Average Relative Error
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