A Study on the Antipicornavirus Activity of Flavonoid Compounds (Flavones) by Using Quantum Chemical and Chemometric Methods. Souza, J., Molfetta, F. A., Honorio, K. M., Santos, R. H. A., & da Silva, A. B. F. J.~Chem.~Inf.~Comput.~Sci., 44:1153--1161, 2004. abstract bibtex The AM1 semiempirical method is employed to calculate a set of molecular properties (variables) of 45 flavone compounds with antipicornavirus activity, and 9 new flavone molecules are used for an activity prediction study. Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Stepwise Discriminant Analysis (SDA), and K-Nearest Neighbor (KNN) are employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying the flavone compounds according to their degree of antipicornavirus activity. The PCA, HCA, SDA, and KNN methods showed that the variables MR (molar refractivity), B9 (bond order between C9 and C10 atoms), and B25 (bond order between C11 and R7 atoms) are important properties for the separation between active and inactive flavone compounds, and this fact reveals that electronic and steric effects are relevant when one is trying to understand the interaction between flavone compounds with antipicornavirus activity and the biological receptor. In the activity prediction study, using the PCA, HCA, SDA, and KNN methodologies, three of the 9 new flavone compounds studied were classified as potentially active against picornaviruses.
@article{Souza:2004aa,
Abstract = { The AM1 semiempirical method is employed to calculate a set of molecular
properties (variables) of 45 flavone compounds with antipicornavirus
activity, and 9 new flavone molecules are used for an activity prediction
study. Principal Component Analysis (PCA), Hierarchical Cluster Analysis
(HCA), Stepwise Discriminant Analysis (SDA), and K-Nearest Neighbor
(KNN) are employed in order to reduce dimensionality and investigate
which subset of variables should be more effective for classifying
the flavone compounds according to their degree of antipicornavirus
activity. The PCA, HCA, SDA, and KNN methods showed that the variables
MR (molar refractivity), B9 (bond order between C9 and C10 atoms),
and B25 (bond order between C11 and R7 atoms) are important properties
for the separation between active and inactive flavone compounds,
and this fact reveals that electronic and steric effects are relevant
when one is trying to understand the interaction between flavone
compounds with antipicornavirus activity and the biological receptor.
In the activity prediction study, using the PCA, HCA, SDA, and KNN
methodologies, three of the 9 new flavone compounds studied were
classified as potentially active against picornaviruses. },
Author = {Souza, J., Jr. and Molfetta, F. A. and Honorio, K. M. and Santos, R. H. A. and da Silva, A. B. F.},
Date-Added = {2007-12-11 17:01:03 -0500},
Date-Modified = {2008-08-27 23:13:28 -0400},
Journal = {J.~Chem.~Inf.~Comput.~Sci.},
Keywords = {qsar; knn; machine learning;},
Owner = {rajarshi},
Pages = {1153--1161},
Timestamp = {2007.04.11},
Title = {A Study on the Antipicornavirus Activity of Flavonoid Compounds (Flavones) by Using Quantum Chemical and Chemometric Methods},
Volume = {44},
Year = {2004}}
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Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Stepwise Discriminant Analysis (SDA), and K-Nearest Neighbor (KNN) are employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying the flavone compounds according to their degree of antipicornavirus activity. The PCA, HCA, SDA, and KNN methods showed that the variables MR (molar refractivity), B9 (bond order between C9 and C10 atoms), and B25 (bond order between C11 and R7 atoms) are important properties for the separation between active and inactive flavone compounds, and this fact reveals that electronic and steric effects are relevant when one is trying to understand the interaction between flavone compounds with antipicornavirus activity and the biological receptor. In the activity prediction study, using the PCA, HCA, SDA, and KNN methodologies, three of the 9 new flavone compounds studied were classified as potentially active against picornaviruses. 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Principal Component Analysis (PCA), Hierarchical Cluster Analysis\n\t(HCA), Stepwise Discriminant Analysis (SDA), and K-Nearest Neighbor\n\t(KNN) are employed in order to reduce dimensionality and investigate\n\twhich subset of variables should be more effective for classifying\n\tthe flavone compounds according to their degree of antipicornavirus\n\tactivity. 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