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• W3138754114 abstract "The aim of the present work was the development of empirical methods for the prediction of properties of organic compounds. The success of finding relationships between structures and properties is dependent on an appropriate structure representation. If a structure is described properly with respect to the property of interest, statistical and pattern recognition methods can be applied in order to find quantitative relationships between structure and properties. In this work, special emphasis was placed on two aspects of the general process of predicting properties by empirical methods. The first aspect was to find an appropriate representation of the molecular charge distribution by the concept of atomic partial charges. Secondly, from a software development's point of view the derivation of calculation algorithms and the management of properties and associated data was a central concern. An integrated programming library, MOSES, was designed and implemented by means of modern software development technologies. An advanced management of properties, calculation modules, and associated metadata was developed. Furthermore, MOSES assists with the development of models for the prediction of properties by a comprehensive set of required functionality such as methods for statistical data analysis, pattern recognition methods, input and output of chemical data and parameter optimization. With two applications, the usability of MOSES was demonstrated. A model for the prediction of mean molecular polarizability was developed based on additivity of atomic hybrid contribution by means of multiple linear-regression analysis. Secondly, an application was presented for the generation and management of workflows typically encountered within the domain of property prediction. The core of the MOSES library, RAMSES, a structure representation of electron systems based on a separation of sigma- and pi-electrons, was extended in order to handle hypervalent compounds, such as sulfonic acids, correctly. This representation was based on an analysis of hypervalent atom types by means of natural bond orbital analysis. The RAMSES datastructure grounds on the basic philosophy incorporated in the Huckel MO theory. Therefore, this datastructure was well suited for the development of a new calculation scheme for the quantification of atomic partial charges. A scheme for the calculation of partial charges is presented that grounds on the well-established PEOE algorithm. For the quantification of picharges, a modified Huckel MO treatment was derived taking into account the inductive effect on the picharge distribution by the sigma-skeleton. It was shown that this modification improves the quality of the calculated picharges considerably. A second modification was introduced for redistributing picharge such as to model the hyperconjugative effect. For the charge calculation schemes, a series of parameters had to be optimized. A thorough analysis was performed in order to identify the best way for achieving the parametrization. From a variety of experimental properties related to the charge distribution and quantum mechanical charge calculation scheme, two parametrizations were achieved. One is based on molecular dipole moments and the other on fitting to DFT/NPA charges. For the fitting to DFT/NPA charges the PEOE algorithm had to be modified. For both parametrizations a high agreement for the fitting was obtained. Charges from both parametrizations give a realistic picture of the molecular charge distribution. This was shown by correlations with ESCA chemical shifts and by the evaluation of picharge-effects in substituted benzene derivatives." @default.
• W3138754114 created "2021-03-29" @default.
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• W3138754114 date "2005-01-01" @default.
• W3138754114 modified "2023-09-28" @default.
• W3138754114 title "Prediction of Properties of Organic Compounds - Emperical Methods and Management of Property Data" @default.
• W3138754114 hasPublicationYear "2005" @default.
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