Partial Least Squares PLS

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. 

PLS (partial least-squares) algorithm used for 3D QSAR calculations PM3 (parameterization method three) a semiempirical method PMF (potential of mean force) a solvation method for molecular dynamics calculations... 

For many applications, quantitative band shape analysis is difficult to apply. Bands may be numerous or may overlap, the optical transmission properties of the film or host matrix may distort features, and features may be indistinct. If one can prepare samples of known properties and collect the FTIR spectra, then it is possible to produce a calibration matrix that can be used to assist in predicting these properties in unknown samples. Statistical, chemometric techniques, such as PLS (partial least-squares) and PCR (principle components of regression), may be applied to this matrix. Chemometric methods permit much larger segments of the spectra to be comprehended in developing an analysis model than is usually the case for simple band shape analyses. 

Partial least squares regression (PLS). Partial least squares regression applies to the simultaneous analysis of two sets of variables on the same objects. It allows for the modeling of inter- and intra-block relationships from an X-block and Y-block of variables in terms of a lower-dimensional table of latent variables [4]. The main purpose of regression is to build a predictive model enabling the prediction of wanted characteristics (y) from measured spectra (X). In matrix notation we have the linear model with regression coefficients b ... 

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-... 

PLS (partial least squares) multiple regression technique is used to estimate contributions of various polluting sources in ambient aerosol composition. The characteristics and performance of the PLS method are compared to those of chemical mass balance regression model (CMB) and target transformation factor analysis model (TTFA). Results on the Quail Roost Data, a synthetic data set generated as a basis to compare various receptor models, is reported. PLS proves to be especially useful when the elemental compositions of both the polluting sources and the aerosol samples are measured with noise and there is a high correlation in both blocks. 

Study 4 A broader range of chemicals fitted by a new technique. A final means of relating physicochemical parameters to MDS spaces is a new technique called PLS (partial least squares,... 

There is a long history of efforts to find simple and interpretable /i and fi functions for various activities and properties (29, 30). The quest for predictive QSAR models started with Hammett s pioneer work to correlate molecular structures with chemical reactivities (30-32). However, the widespread applications of modern predictive QSAR and QSPR actually started with the seminal work of Hansch and coworkers on pesticides (29, 33, 34) and the developments of various powerful analysis tools, such as PLS (partial least squares) and neural networks, for multivariate analysis have fueled these widespread applications. Nowadays, numerous publications on guidelines, workflows, and... 

This method makes use of a test battery to derive a toxicity index that can be employed to classify effluents as a function of their overall toxicity. A formula is given as an example and a procedure to calculate the index using expert judgements and a PLS (Partial Least Square) regression procedure is described using data on 30 effluents. 

SIMCA EMX, P.O.Box 336, S-95125 Lulea, Sweden 2200. Multivariate data analysis by SIMCA (principal component models of classes) and PLS (partial least square) (ref. 20). 

The PCR method (and PLS, partial least squares, discussed in the Section 6.7) assumes that the linear relation (Equation 6.10) between the x- and y-variables is in fact a bilinear model that depends on scores t ... 

Perform experimental tests on this subset of compounds and then use some form of modelling to relate the desired activity to structural data. Note that this modelling does not have to be multilinear modelling as discussed in this section, but could also be PLS (partial least squares) as introduced in Chapter 5. 

This work was supported by a grant from the National Science Foundation, t Abbreviations used are as follows. FTIR Fourier transform infrared spectroscopy, ATR attenuated total reflectance, IRE internal reflection element, SATR solution ATR FTIR, FSD Fourier self-deconvolution, PLS partial least-squares analysis, PRESS prediction residual sum of squares from PLS. SECV standard error of calibration values from PLS, PLSl PLS analysis in which each component is predicted independently, PLS2 PLS analysis in which all components are predicted simultaneously. 

The multiple regression method is most often employed to derive predictive QSRR. However, good predictions of GC retention were obtained by means of factorial methods of data analysis. The PLS (partial least squares) treatment of 17 simple descriptors of analytes, such as the number of atoms of each element, of multiple bonds, of functional groups, etc., made predictions of retention of 100 substituted benzenes and pyridines 188],... 

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