1.
Sato, Fernando; Braga, Scheila F; Santos, Helio F dos; Galvao, Douglas S
Structure-Activity Relationship Investigation of Some New Tetracyclines by Electronic Index Methodology Journal Article
In: arXiv preprint arXiv:0708.2931, 2007.
@article{sato2007structure,
title = {Structure-Activity Relationship Investigation of Some New Tetracyclines by Electronic Index Methodology},
author = {Sato, Fernando and Braga, Scheila F and Santos, Helio F dos and Galvao, Douglas S},
url = {http://arxiv.org/abs/0708.2931},
year = {2007},
date = {2007-01-01},
journal = {arXiv preprint arXiv:0708.2931},
abstract = {Tetracyclines are an old class of molecules that constitute a broad-spectrum antibiotics. Since the first member of tetracycline family were isolated, the clinical importance of these compounds as therapeutic and prophylactic agents against a wide range of infections has stimulated efforts to define their mode of action as inhibitors of bacterial reproduction. We used three SAR methodologies for the analysis of biological activity of a set of 104 tetracycline compounds. Our calculation were carried out using the semi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3). Electronic Indices Methodology (EIM), Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) were applied to the classification of 14 old and 90 new proposed derivatives of tetracyclines. Our results make evident the importance of EIM descriptors in pattern recognition and also show that the EIM can be effectively used to predict the biological activity of Tetracyclines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tetracyclines are an old class of molecules that constitute a broad-spectrum antibiotics. Since the first member of tetracycline family were isolated, the clinical importance of these compounds as therapeutic and prophylactic agents against a wide range of infections has stimulated efforts to define their mode of action as inhibitors of bacterial reproduction. We used three SAR methodologies for the analysis of biological activity of a set of 104 tetracycline compounds. Our calculation were carried out using the semi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3). Electronic Indices Methodology (EIM), Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) were applied to the classification of 14 old and 90 new proposed derivatives of tetracyclines. Our results make evident the importance of EIM descriptors in pattern recognition and also show that the EIM can be effectively used to predict the biological activity of Tetracyclines.
2007
1.
Sato, Fernando; Braga, Scheila F; Santos, Helio F dos; Galvao, Douglas S
Structure-Activity Relationship Investigation of Some New Tetracyclines by Electronic Index Methodology Journal Article
In: arXiv preprint arXiv:0708.2931, 2007.
Abstract | Links | BibTeX | Tags: Drug Design, Electronic Structure, Neural Networks, PCA/HCA, Tetracyclines, Theory of Electronic Indices
@article{sato2007structure,
title = {Structure-Activity Relationship Investigation of Some New Tetracyclines by Electronic Index Methodology},
author = {Sato, Fernando and Braga, Scheila F and Santos, Helio F dos and Galvao, Douglas S},
url = {http://arxiv.org/abs/0708.2931},
year = {2007},
date = {2007-01-01},
journal = {arXiv preprint arXiv:0708.2931},
abstract = {Tetracyclines are an old class of molecules that constitute a broad-spectrum antibiotics. Since the first member of tetracycline family were isolated, the clinical importance of these compounds as therapeutic and prophylactic agents against a wide range of infections has stimulated efforts to define their mode of action as inhibitors of bacterial reproduction. We used three SAR methodologies for the analysis of biological activity of a set of 104 tetracycline compounds. Our calculation were carried out using the semi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3). Electronic Indices Methodology (EIM), Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) were applied to the classification of 14 old and 90 new proposed derivatives of tetracyclines. Our results make evident the importance of EIM descriptors in pattern recognition and also show that the EIM can be effectively used to predict the biological activity of Tetracyclines.},
keywords = {Drug Design, Electronic Structure, Neural Networks, PCA/HCA, Tetracyclines, Theory of Electronic Indices},
pubstate = {published},
tppubtype = {article}
}
Tetracyclines are an old class of molecules that constitute a broad-spectrum antibiotics. Since the first member of tetracycline family were isolated, the clinical importance of these compounds as therapeutic and prophylactic agents against a wide range of infections has stimulated efforts to define their mode of action as inhibitors of bacterial reproduction. We used three SAR methodologies for the analysis of biological activity of a set of 104 tetracycline compounds. Our calculation were carried out using the semi-empirical Austin Method One (AM1) and Parametric Method 3 (PM3). Electronic Indices Methodology (EIM), Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) were applied to the classification of 14 old and 90 new proposed derivatives of tetracyclines. Our results make evident the importance of EIM descriptors in pattern recognition and also show that the EIM can be effectively used to predict the biological activity of Tetracyclines.
http://scholar.google.com/citations?hl=en&user=95SvbM8AAAAJ
