Partial least squares projections to latent structure

E Johansson and M Cocchi 1993. PLS - Partial Least-squares Projections to Latent Structures. In binyi H (Editor) 3D QSAR in Drug Design. Leiden, ESCOM, pp. 523-550. 

Norinder, U., Osterberg, T. Theoretical calculation and prediction of drug transport processes using simple parameters and partial least squares projections to latent structures (PLS) statistics. The use of electrotopological state indices./. Pharm. Sci. 2001, 90, 1075-1085. 

PLS Partial least squares projection to latent structures... 

Wold, S., Johansson, E., Cocchi, M., PLS - Partial least-squares projections to latent structures, in 3D QSAR in Drug Design. Kubinyi, H. (ed.). ESCOM, Leiden, 1993, pp. 523-... 

There are apparently many multivariate statistical methods partly overlapping in scope [11]. For most problems occurring in practice, we have found the use of two methods sufficient, as discussed below. The first method is called principal component analysis (PCA) and the second is the partial least-squares projection to latent structures (PLS). A detailed description of the methods is given in Appendix A. In the following, a brief description is presented. 

When compounds are selected according to SMD, this necessitates the adequate description of their structures by means of quantitative variables, "structure descriptors". This description can then be used after the compound selection, synthesis, and biological testing to formulate quantitative models between structural variation and activity variation, so called Quantitative Structure Activity Relationships (QSARs). For extensive reviews, see references 3 and 4. With multiple structure descriptors and multiple biological activity variables (responses), these models are necessarily multivariate (M-QSAR) in their nature, making the Partial Least Squares Projections to Latent Structures (PLS) approach suitable for the data analysis. PLS is a statistical method, which relates a multivariate descriptor data set (X) to a multivariate response data set Y. PLS is well described elsewhere and will not be described any further here [42, 43]. 

PCM modeling aims to find an empirical relation (a PCM equation or model) that describes the interaction activities of the biopolymer-molecule pairs as accurate as possible. To this end, various linear and nonlinear correlation methods can be used. Nonlinear methods have hitherto been used to only a limited extent. The method of prime choice has been partial least-squares projection to latent structures (PLS), which has been found to work very satisfactorily in PCM. PCA is also an important data-preprocessing tool in PCM modeling. Modeling includes statistical model-validation techniques such as cross validation, external prediction, and variable-selection and signal-correction methods to obtain statistically valid models. (For general overviews of modeling methods see [10]). 

Wold S, Johansson E, Cocchi M. PLS-partial least squares projections to latent structures. In Kubinyi H, editor. 3D-QSAR in drug design Theory, methods and applications. Leiden ESCOM, 1993. p. 523-50. 

Partial least square projection to latent structures Polar surface area... 

Abbreviations used sigmoidal regression (SR), classification and regression trees (CART), partial least square projection to latent structure... 

PLS Partial Least Squares Projections to Latent Structures... 

Wold S, Sjostrom M, Eriksson L. Partial Least Squares Projections to Latent Structures (PLS). In Schleyer PvR, Allinger NL, Clark T, Gasteiger J, Kollman PA, Schaefer HF III, Schreiner PR, eds. Encyclopedia of Computational Chemistry. Vol. 4. Chichester, UK John Wiley and Sons Ltd., 1998 2006-2021. 

MLR - multiple linear regression, PLS - partial least squares projection to latent structures, NN - neural network. 

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