Data mining with QSAR

In the last decades not only thousands of chemical descriptors but also many advanced, powerful modeling algorithms have been made available, The older QSAR models were linear equations with one or a few parameters. Then, other tools have been introduced, such as artificial neural network, fuzzy logic, and data mining algorithms, making possible non linear models and automatic generation of mathematical solutions. 

In this chapter, we will give a brief introduction to the basic concepts of chemoinformatics and their relevance to chemical library design. In Section 2, we will describe chemical representation, molecular data, and molecular data mining in computer we will introduce some of the chemoinformatics concepts such as molecular descriptors, chemical space, dimension reduction, similarity and diversity and we will review the most useful methods and applications of chemoinformatics, the quantitative structure-activity relationship (QSAR), the quantitative structure-property relationship (QSPR), multiobjective optimization, and virtual screening. In Section 3, we will outline some of the elements of library design and connect chemoinformatics tools, such as molecular similarity, molecular diversity, and multiple objective optimizations, with designing optimal libraries. Finally, we will put library design into perspective in Section 4. 

QSRR studies in MEKC involving LSS with LSD are scarce and not helpful in delineating solute-micelle interactions but are useful for predictive purposes (22). Typically, LSD are screened in quantitative structure activity relationships (QSAR) studies where the exact nature of the relationship between the solute structure and biological activity is hardly established by a physicochemical model. Just for reference, data mining procedures for LSD and statistical modehng aspects of QSRR using chromatographic data have been reviewed recently (23). 

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