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Megavariate analysis of environmental QSAR data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD)

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Abstract

This paper introduces principal component analysis (PCA), partial least squares projections to latent structures (PLS), and statistical molecular design (SMD) as useful tools in deriving multi- and megavariate quantitative structure-activity relationship (QSAR) models. Two QSAR data sets from the fields of environmental toxicology and environmental chemistry are worked out in detail, showing the benefits of PCA, PLS and SMD. PCA is useful when overviewing a data set and exploring relationships among compounds and relationships among variables. PLS is the regression extension of PCA and is used for establishing QSARs. SMD is essential for selecting informative training and test sets of compounds for QSAR calibration and validation.

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Abbreviations

CC:

canonical correlation

DOOD:

D-optimal onion design

FA:

factor analysis

FD:

factorial design

FFD:

fractional factorial design

HOMO:

highest occupied molecular orbital

LDA:

linear discriminant analysis

LUMO:

lowest unoccupied molecular orbital

MLR:

multiple linear regression

NN:

neural networks

PCA:

principal component analysis

PCB:

polychlorinated biphenyls

PCR:

principal component regression

PLS:

partial least squares projections to latent structures

PLS-DA:

PLS discriminant analysis

QSAR:

quantitative structure-activity relationships

RMSEE:

root mean square error of estimation

RMSEP:

root mean square error of prediction

RR:

ridge regression

SIMCA:

soft independent modelling of class analogy

SQRT:

square root

SMD:

statistical molecular design

SVM:

support vector machines

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Authors and Affiliations

  1. Umetrics AB, POB 7960, S-907 19, Umeå, Sweden

    Lennart Eriksson & Erik Johansson

  2. Institute of Environmental Chemistry, Department of Chemistry, Umeå University, S-901 87, Umeå, Sweden

    Patrik L. Andersson & Mats Tysklind

Authors
  1. Lennart Eriksson
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  2. Patrik L. Andersson
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  3. Erik Johansson
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  4. Mats Tysklind
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Correspondence to Lennart Eriksson.

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Eriksson, L., Andersson, P.L., Johansson, E. et al. Megavariate analysis of environmental QSAR data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD). Mol Divers 10, 169–186 (2006). https://doi.org/10.1007/s11030-006-9024-6

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