Large descriptor spaces

QSAR of Heterocyclic Compounds in Large Descriptor Spaces... 

QSAR/QSPR Applications in Large Descriptors Spaces... 

SELECTION OF THE BEST STATISTICAL QSAR MODELS IN LARGE DESCRIPTOR SPACES... 

The final result has therefore the maximum value of the Fisher criterion and the highest value of the cross-validated correlation coefficient. According to these statistical criteria, it was considered as the best representation of the property in the given (large) descriptor space. The BMLR approach has a variation that takes care of the noncoUinearity of descriptors pairs, called the Heuristics method (1996JPC10400). The advantages of such methods are that they are fast and limit the chance correlation to minimum. Both techniques were successfully used by ARK for model building for a tremendous amount of chemical properties of compounds and heterocycHcs, in particular. 

The development of QSAR/QSPR on large descriptor spaces started some time ago. One of the first widely applicable programs for such modeling was CODESSA (Comprehensive DEscriptors for Structural and Statistical Analysis), developed by one of us (M.K.) and Victor Lobanov, a graduate student at that time, in collaboration with Professor Alan R. Katritzky at the University of Florida. In 1994, the first version of the program was published and assessed for the treatment of a variety of chemical and physical properties of compounds and heterocyclic compounds, in particular (1995CSR279, 1997JCICS405). 

QSAR/QSPR APPLICATIONS IN LARGE DESCRIPTORS SPACES... 

Progress in heterocyclic metaUosupramolecular constmction is reviewed by James Ludlow and George Newkome (University of Akron) in Chapter 6. They discuss in particular hierarchical self-assembly and interconversion between supramolecular structures. In Chapter 7, Mati Karelson (University of Tartu) and Dimitar Dobchev (Tallin University of Technology) describe the QSAR approach applied to heterocycles where large descriptor spaces... 

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