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.
2.
Coluci, Vitor Rafael; Vendrame, Rosana; Braga, RS; Galvao, DS
In: Journal of chemical information and computer sciences, vol. 42, no. 6, pp. 1479–1489, 2002.
@article{coluci2002identifying,
title = {Identifying relevant molecular descriptors related to carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs) using pattern recognition methods},
author = {Coluci, Vitor Rafael and Vendrame, Rosana and Braga, RS and Galvao, DS},
url = {http://pubs.acs.org/doi/abs/10.1021/ci025577%2B},
year = {2002},
date = {2002-01-01},
journal = {Journal of chemical information and computer sciences},
volume = {42},
number = {6},
pages = {1479--1489},
publisher = {American Chemical Society},
abstract = {Polycyclic Aromatic Hydrocarbons (PAHs) constitute an important family of molecules capable of inducing
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Polycyclic Aromatic Hydrocarbons (PAHs) constitute an important family of molecules capable of inducing
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.
2005
2.
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.
2002
1.
Coluci, Vitor Rafael; Vendrame, Rosana; Braga, RS; Galvao, DS
In: Journal of chemical information and computer sciences, vol. 42, no. 6, pp. 1479–1489, 2002.
Abstract | Links | BibTeX | Tags: Carcinogenesis, Neural Networks, PCA/HCA, Polycyclic Aromatic Hydrocarbons (PAHs), Theory of Electronic Indices
@article{coluci2002identifying,
title = {Identifying relevant molecular descriptors related to carcinogenic activity of polycyclic aromatic hydrocarbons (PAHs) using pattern recognition methods},
author = {Coluci, Vitor Rafael and Vendrame, Rosana and Braga, RS and Galvao, DS},
url = {http://pubs.acs.org/doi/abs/10.1021/ci025577%2B},
year = {2002},
date = {2002-01-01},
journal = {Journal of chemical information and computer sciences},
volume = {42},
number = {6},
pages = {1479--1489},
publisher = {American Chemical Society},
abstract = {Polycyclic Aromatic Hydrocarbons (PAHs) constitute an important family of molecules capable of inducing
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.},
keywords = {Carcinogenesis, Neural Networks, PCA/HCA, Polycyclic Aromatic Hydrocarbons (PAHs), Theory of Electronic Indices},
pubstate = {published},
tppubtype = {article}
}
Polycyclic Aromatic Hydrocarbons (PAHs) constitute an important family of molecules capable of inducing
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.
chemical carcinogenesis. In this work we report structure-activity relationship (SAR) studies for 81 PAHs
using the pattern-recognition methods Principal Component Analysis (PCA), Hierarchical Clustering Analysis
(HCA) and Neural Networks (NN). The used molecular descriptors were obtained from the semiempirical
Parametric Method 3 (PM3) calculations. We have developed a new procedure that is capable of identifying
the PAHs’ carcinogenic activity with an accuracy higher than 80%. PCA selected molecular descriptors
that can be directly correlated with some models proposed to PAHs’ metabolic activation mechanism leading
to the formation of PAHs-DNA adducts. PCA, HCA and NN validate the energy separation between the
highest occupied molecular orbital and its next lower level as a major descriptor defining the carcinogenic
activity. This descriptor has been only recently discussed in the literature as one new possible universal
parameter for defining the biological activity of several classes of compounds.
http://scholar.google.com/citations?hl=en&user=95SvbM8AAAAJ
