CARSO procedure

In the first case, the structural description of over 100 thienyl- and furyl-benzimidazoles and benzoxazoles was multivariately characterized to identify three latent variables. A set of 16 informative molecules was derived thereafter on applying a central composite design criterion in these latent variables to all the available structures. The data were analyzed by a linear PLS model, which permitted the optimization of three structural features out of four. The fourth one, the substituent linked to the homocyclic ring of the bicyclic system was finally optimized by the CARSO procedure in terms of the substituents PPs, predicting two new compounds as possible optimal structures. Indeed, later analysis revealed the accuracy of these predictions. 

Recently, a nonlinear version of PLS analysis was described [616]. The CARSO (computer-aided response surface optimization) procedure [617, 618] aims at obtaining response surfaces for non-designed data sets. Quadratic terms and interaction terms are generated for each independent variable and PLS analysis is used to model the data, due to the fact that regression analysis will fail for data sets with many highly interrelated variables in the X block. 

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