Summary
A chemometric strategy for quantitative structure-activity and structure-property relationship (QSAR, QSPR) analysis in environmental chemistry is outlined. In essence, the strategy is based upon grouping chemicals into homogeneous classes, and identifying small numbers of training set and validation set compounds that adequately represent each class. Biological and environmental response data generated for such sets of representative compounds may then be sufficient for constructing statistically sound models relating the variation in responses to the differences in chemical properties. The use of such QSAR or QSPR models may also allow the prediction of missing response data for untested compounds in the relevant class, and thus enable priority setting for further biological or environmental testing in relation to the predicted severity of these responses. Four representative examples of how QSAR and QSPR can be formulated for environmentally relevant classes of compounds and meaningful responses are addressed. Two of the illustrated applications are also developed within the framework of the chemometric QSAR strategy.
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Abbreviations
- ANOVA:
-
analysis of variance
- BCF:
-
bioconcentration factor
- CFCs:
-
chlorinated fluorocarbons
- CV:
-
crossvalidation
- DV:
-
design variable
- EC50 :
-
effect concentration (50% effect)
- EROD:
-
ethoxyresorufin-O-deethylase
- FD:
-
factorial design
- FFD:
-
fractional factorial design
- HPLC:
-
high performance liquid chromatography
- IC50 :
-
inhibitory concentration (50% effect)
- IR:
-
infrared spectroscopy
- KNN:
-
K nearest neighbours
- LC:
-
liquid chromatography
- LDA:
-
linear discriminant analysis
- LD50 :
-
lethal dose (50% effect)
- LR:
-
linear regression
- MLR:
-
multiple linear regression
- NBP:
-
4-nitrobenzylpyridine
- NMR:
-
nuclear magnetic resonance spectroscopy
- NN:
-
neural networks
- OCDF:
-
octachlorodibenzofuran
- PCA:
-
principal component analysis
- PCDFs:
-
polychlorinated dibenzofurans
- PCR:
-
principal component regression
- PCs:
-
principal components
- PLS:
-
partial least squares
- PLS-DA:
-
partial least squares discriminant analysis
- PRESS:
-
prediction error sum of squares
- RR:
-
ridge regression
- QSAR:
-
quantitative structure-activity relationships
- QSPR:
-
quantitative structure-property relationships
- SS y :
-
sum of squares of responses (y)
- TCDD:
-
2,3,7,8-tetrachlorodioxin
- TEF:
-
toxic equivalency factor
- UV:
-
ultraviolet spectroscopy
- B PLS :
-
PLS pseudo-regression coefficients
- C :
-
loading matrix of PLS
- E :
-
residual matrix
- F :
-
residual matrix
- ̄ :
-
variable average
- s 2 :
-
variable variance
- K :
-
number of descriptor variables
- M :
-
number of response variables
- N :
-
number of chemicals in the training set
- P :
-
loading matrix of PCA or PLS
- Q 2 :
-
predicted variance
- R 2 :
-
explained variance
- R 2 cv :
-
predicted variance
- T :
-
score matrix of PCA or PLS
- U :
-
score matrix of PLS
- W :
-
weight matrix of PLS
- X :
-
matrix of descriptor variables
- Y :
-
matrix of response variables
- β :
-
regression coefficients
- bp:
-
boiling point
- D E :
-
electrophilic delocalizability
- D N :
-
nucleophilic delocalizability
- E HOMO :
-
energy of highest occupied molecular orbital
- E LUMO :
-
energy of lowest unoccupied molecular orbital
- E s :
-
Taft substituent constant
- k NPB :
-
chemical reactivity of epoxides
- K ow :
-
partition coefficient of octanol/water
- mp:
-
melting point
- Mw :
-
molecular weight
- n D :
-
refractive index
- p :
-
bond order
- pK a :
-
acid dissociation constant
- q :
-
atomic charge
- TSA:
-
total surface area
- TMV:
-
total molar volume
- V vdW :
-
van der Waals volume
- p :
-
density
- σ :
-
Hammett sigma constant
- µ :
-
electronegativity
- ŋ :
-
hardness
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Eriksson, L., Hermens, J.L.M. (1995). A Multivariate Approach to Quantitative Structure-Activity and Structure-Property Relationships. In: Einax, J. (eds) Chemometrics in Environmental Chemistry - Applications. The Handbook of Environmental Chemistry, vol 2 / 2H. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49150-7_5
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DOI: https://doi.org/10.1007/978-3-540-49150-7_5
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