Development of QSAR model

Tang, W.Z. and Hendrix, T., Development of QSAR models to predict kinetic rate constants for elementary hydroxyl radical reactions, in Water and Wastewater Industry and Sustainable Development In Honor of Professor Xu Baojiu s 80th Anniversary, Tsinghua University Press, Beijing, China, 1998, pp. 44CM51. 

Vaca, L., Development of QSAR Models for Oxidation Rate Constants in Supercritical Water Oxidation, M.S. thesis, Florida International University, Miami, 1999. 

Guha R, Jurs PC (2004) The development of QSAR models to predict and interpret the biological activity of artemisinin analogues. J Chem Inf Comput Sci 44 1440-1449... 

Basak, S.C. and Mills, D. (2001c) Use of mathematical structural invariants in the development of QSAR models. MATCH Commun. Math. Comput. Chem., 44, 15-30. 

Cedeno, W. and Agrafiotis, D.K. (2003) Using particle swarms for the development of QSAR models based on fC-nearest neighbor and kernel regression./. Comput. Aid. Mol Des., 17, 255-263. 

F or the characterization of the charge distribution in a molecule, the numerically unitary integrated molecular transform (FTm), its analogous electronic (FTe) and charge (FTC) transforms, and the normalized molecular moment (Mn), its analogous electronic (AQ and charge (Mc) moment, have been developed as molecular structure descriptors [45,46]. Those descriptors have been applied successfully for the development of QSAR models for various physicochemical, pharmacological, and thermodynamic properties of compounds. 

With a lead in hand, the first problem is the selection of the initial set of substituents to make. Ideally, this step involves selection of a set of substituents that represents the whole universe of factors involved in the activity of the lead compound and does so with the smallest number of compounds possible. If this step is not done well, there is a high probability that the resultant QSAR analysis will not be successful. For example, Martin and Panas present evidence that poor series design can be the cause of a failure to find a QSAR model for activity. Conversely, adoption of strategies for the proper selection of substituent sets may be all that is needed to dramatically increase the efficiency of the design process and ensure the development of QSAR models. Indeed Martinhas suggested It is the opinion of many in the QSAR field that if the only contribution of QSAR to medicinal chemistry were to emphasize the importance of objective Series design, this in itself would be a major contribution to increased efficiency. ... 

Statistical methods are the mathematical basis for the development of QSAR models. Chemometiic methods (Eriksson et al., 2001) are used to extract information from QSAR data using tools of statistics and mathematics. The applications of these methods are combined with the important goal of explanation and prediction of non-synthesised test compounds. Many different statistical methods are available in the literature and the selection of the appropriate method is critical (Xu and Zhang, 2001). 

J. B. Bhonsle, A. K. Bhattacharjee, and R. K. Gupta, Novel semi-automated methodology for developing highly predictive QSAR models Appbcation for development of QSAR models for insect repellent amides. Journal of Molecular Modeling, 13, 179-208, 2007. 

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