Analyzing Data QSAR

As a multivariate statistical method, partial least square (PLS) is of particular interest in the QSAR field [3]. PLS can analyze data with strongly collinear, noisy, and numerous X variables, while simultaneously modeling several response variables Y. PLS can also provide several prediction regions and diagnostic plots as statistical measures. Using such an approach, QSAR scientists can extract the patterns embedded in the structure-activity data. 

For a QSAR analysis a training set of compounds with known descriptor properties (e. g. pKa-values, surface areas, dipole moments etc.), including the property of interest, is required. The Hansch Analysis1461 is a statistical method to analyze and correlate these data in order to determine the magnitude of the target property (Eq. 2.15). 

On the basis of the 3D structures of the proteins, the GRID/GPGA method analyzes the selectivity differences from the viewpoint of the target and is therefore independent of the availability of appropriate ligand binding data for a ligand-based QSAR analysis. 

Young, S.S. and Hawkins, D.M. (1998). Using Recursive Partitioning to Analyze a Large SAR Data Set. SAR QSAR Environ.Res., 8,183-193. 

Hansch and Caldwell have analyzed the quantitative structure/activity relationships (QSAR) of a series of amphetamine and 2-phenethylamine analogs, to discern the role of steric and hydrophobic aryl substituents on the inhibition of 5-HT uptake (142). From the biological data of 19 compounds, including those in Table 15.13. and some additional analogs, the following equation was derived for inhibition of uptake activity, where C is the IC concentration, MR4 is the molar refrac-tivity value of the aryl substituent scaled by 0.1, and 7T3 is the hydrophobicity of the meUi substituent on the aryl ring ... 

Soffers AE, Boersma MG, Vaes WH, Vervoort J, Tyrakowska B, Hermens JL, et al. Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity. Toxicol In Vitro 2001 15 539-51. 

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