1.
Troche, Karla S; Braga, Scheila F; Coluci, Vitor R; Galvao, Douglas S
Carcinogenic classification of polycyclic aromatic hydrocarbons through theoretical descriptors Journal Article
In: International journal of quantum chemistry, vol. 103, no. 5, pp. 718–730, 2005.
@article{troche2005carcinogenic,
title = {Carcinogenic classification of polycyclic aromatic hydrocarbons through theoretical descriptors},
author = {Troche, Karla S and Braga, Scheila F and Coluci, Vitor R and Galvao, Douglas S},
url = {http://onlinelibrary.wiley.com/doi/10.1002/qua.20529/full},
year = {2005},
date = {2005-01-01},
journal = {International journal of quantum chemistry},
volume = {103},
number = {5},
pages = {718--730},
publisher = {Wiley Online Library},
abstract = {Polycyclic aromatic hydrocarbons (PAHs) constitute an importantfamily of molecules capable of inducing chemical carcinogenesis. In this work we reporta comparative structure–activity relationship (SAR) study for 81 PAHs using differentmethodologies. The recently developed electronic indices methodology (EIM) withquantum descriptors obtained from different semiempirical methods (AM1, PM3, andPM5) was contrasted against more standard pattern recognition methods (PRMs),principal component analysis (PCA), hierarchical cluster analysis (HCA), Kth nearestneighbor (KNN), soft independent modeling of class analogies (SIMCA), and neuralnetworks (NN). Our results show that PRMs validate the statistical value of electronicparameters derived from EIM analysis and their ability to identify active compounds.EIM outperformed more standard SAR methodologies and does not appear to besignificantly Hamiltonian-dependent.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Polycyclic aromatic hydrocarbons (PAHs) constitute an importantfamily of molecules capable of inducing chemical carcinogenesis. In this work we reporta comparative structure–activity relationship (SAR) study for 81 PAHs using differentmethodologies. The recently developed electronic indices methodology (EIM) withquantum descriptors obtained from different semiempirical methods (AM1, PM3, andPM5) was contrasted against more standard pattern recognition methods (PRMs),principal component analysis (PCA), hierarchical cluster analysis (HCA), Kth nearestneighbor (KNN), soft independent modeling of class analogies (SIMCA), and neuralnetworks (NN). Our results show that PRMs validate the statistical value of electronicparameters derived from EIM analysis and their ability to identify active compounds.EIM outperformed more standard SAR methodologies and does not appear to besignificantly Hamiltonian-dependent.
2005
1.
Troche, Karla S; Braga, Scheila F; Coluci, Vitor R; Galvao, Douglas S
Carcinogenic classification of polycyclic aromatic hydrocarbons through theoretical descriptors Journal Article
In: International journal of quantum chemistry, vol. 103, no. 5, pp. 718–730, 2005.
Abstract | Links | BibTeX | Tags: Carcinogenesis, HCA, Neural Networks, PCA, Polycyclic Aromatic Hydrocarbons (PAHs), Theory of Electronic Indices
@article{troche2005carcinogenic,
title = {Carcinogenic classification of polycyclic aromatic hydrocarbons through theoretical descriptors},
author = {Troche, Karla S and Braga, Scheila F and Coluci, Vitor R and Galvao, Douglas S},
url = {http://onlinelibrary.wiley.com/doi/10.1002/qua.20529/full},
year = {2005},
date = {2005-01-01},
journal = {International journal of quantum chemistry},
volume = {103},
number = {5},
pages = {718--730},
publisher = {Wiley Online Library},
abstract = {Polycyclic aromatic hydrocarbons (PAHs) constitute an importantfamily of molecules capable of inducing chemical carcinogenesis. In this work we reporta comparative structure–activity relationship (SAR) study for 81 PAHs using differentmethodologies. The recently developed electronic indices methodology (EIM) withquantum descriptors obtained from different semiempirical methods (AM1, PM3, andPM5) was contrasted against more standard pattern recognition methods (PRMs),principal component analysis (PCA), hierarchical cluster analysis (HCA), Kth nearestneighbor (KNN), soft independent modeling of class analogies (SIMCA), and neuralnetworks (NN). Our results show that PRMs validate the statistical value of electronicparameters derived from EIM analysis and their ability to identify active compounds.EIM outperformed more standard SAR methodologies and does not appear to besignificantly Hamiltonian-dependent.},
keywords = {Carcinogenesis, HCA, Neural Networks, PCA, Polycyclic Aromatic Hydrocarbons (PAHs), Theory of Electronic Indices},
pubstate = {published},
tppubtype = {article}
}
Polycyclic aromatic hydrocarbons (PAHs) constitute an importantfamily of molecules capable of inducing chemical carcinogenesis. In this work we reporta comparative structure–activity relationship (SAR) study for 81 PAHs using differentmethodologies. The recently developed electronic indices methodology (EIM) withquantum descriptors obtained from different semiempirical methods (AM1, PM3, andPM5) was contrasted against more standard pattern recognition methods (PRMs),principal component analysis (PCA), hierarchical cluster analysis (HCA), Kth nearestneighbor (KNN), soft independent modeling of class analogies (SIMCA), and neuralnetworks (NN). Our results show that PRMs validate the statistical value of electronicparameters derived from EIM analysis and their ability to identify active compounds.EIM outperformed more standard SAR methodologies and does not appear to besignificantly Hamiltonian-dependent.
http://scholar.google.com/citations?hl=en&user=95SvbM8AAAAJ
