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G. Idakwo et al.
Abbreviations
1D
One-dimensional
2D
Two-dimensional
3D
Three-dimensional
4D
Four-dimensional
ACO
Ant colony optimization
ECFP
Extended connectivity fingerprints
GA
Genetic algorithm
KPCA
Kernel principal component analysis
LASSO Least absolute shrinkage and selection operator
LDA
Linear discriminant analysis
LOOCV Leave-one-out cross-validation
MACCS Molecular access system
MDS
Multi-dimensional scaling
PCA
Principal component analysis
PSO
Particle swarm optimization
QSAR
Quantitative structure–activity relationship
QSTR
Quantitative structure–toxicity relationship
RFE
Recursive feature elimination
SA
Simulated annealing
SAR
Structure–activity relationship
SFFS
Sequential floating forward selection
SFS
Sequential forward selection
STR
Structure–toxicity relationship
SVM
Support vector machine
Tox21
Toxicology in the twenty-first century
t-SNE
t-Distributed stochastic neighbor embedding
7.1 Introduction
The limitations of in vivo and in vitro approaches for determination of the biological
activity of chemicals have fostered the development of in silico approaches [1]. In
silico predictive toxicology is designed to complement experimental efforts with a
view toward improving the quality of toxicity predictions for safety assessment while
decreasing the associated time, cost, and ethical conflicts (animal testing) [2–4].
Methodology for in silico predictive toxicology has been dominated by (quantitative) structure–activity or toxicity relationship [(Q)SAR or (Q)STR] (hereafter called
SAR). Traditional SAR models describe a relationship between the chemical structure of molecules (numerically encoded as molecular descriptors) and their activity
against a specific biological target [1]. This is achieved by establishing a trend in the
molecular descriptor space that links to a biological activity. Thus, all SAR models
G. Idakwo et al.
Abbreviations
1D
One-dimensional
2D
Two-dimensional
3D
Three-dimensional
4D
Four-dimensional
ACO
Ant colony optimization
ECFP
Extended connectivity fingerprints
GA
Genetic algorithm
KPCA
Kernel principal component analysis
LASSO Least absolute shrinkage and selection operator
LDA
Linear discriminant analysis
LOOCV Leave-one-out cross-validation
MACCS Molecular access system
MDS
Multi-dimensional scaling
PCA
Principal component analysis
PSO
Particle swarm optimization
QSAR
Quantitative structure–activity relationship
QSTR
Quantitative structure–toxicity relationship
RFE
Recursive feature elimination
SA
Simulated annealing
SAR
Structure–activity relationship
SFFS
Sequential floating forward selection
SFS
Sequential forward selection
STR
Structure–toxicity relationship
SVM
Support vector machine
Tox21
Toxicology in the twenty-first century
t-SNE
t-Distributed stochastic neighbor embedding
7.1 Introduction
The limitations of in vivo and in vitro approaches for determination of the biological
activity of chemicals have fostered the development of in silico approaches [1]. In
silico predictive toxicology is designed to complement experimental efforts with a
view toward improving the quality of toxicity predictions for safety assessment while
decreasing the associated time, cost, and ethical conflicts (animal testing) [2–4].
Methodology for in silico predictive toxicology has been dominated by (quantitative) structure–activity or toxicity relationship [(Q)SAR or (Q)STR] (hereafter called
SAR). Traditional SAR models describe a relationship between the chemical structure of molecules (numerically encoded as molecular descriptors) and their activity
against a specific biological target [1]. This is achieved by establishing a trend in the
molecular descriptor space that links to a biological activity. Thus, all SAR models