Combining selectivity and affinity predictions using an integrated
Support Vector Machine (SVM) approach: An alternative tool to discriminate
between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine
antagonists binding sites
L. Michielan, C. Bolcato, S. Federico, B. Cacciari, M. Bacilieri, K. Klotz, S. Kachler, G. Pastorin, R. Cardin, A. Sperduti, G. Spalluto, and S. Moro. Bioorg Med Chem, 17 (14):
5259-74(July 2009)Michielan, Lisa Bolcato, Chiara Federico, Stephanie Cacciari, Barbara
Bacilieri, Magdalena Klotz, Karl-Norbert Kachler, Sonja Pastorin,
Giorgia Cardin, Riccardo Sperduti, Alessandro Spalluto, Giampiero
Moro, Stefano Research Support, Non-U.S. Gov't England Bioorganic
& medicinal chemistry Bioorg Med Chem. 2009 Jul 15;17(14):5259-74.
Epub 2009 May 21..
Abstract
G Protein-coupled receptors (GPCRs) selectivity is an important aspect
of drug discovery process, and distinguishing between related receptor
subtypes is often the key to therapeutic success. Nowadays, very
few valuable computational tools are available for the prediction
of receptor subtypes selectivity. In the present study, we present
an alternative application of the Support Vector Machine (SVM) and
Support Vector Regression (SVR) methodologies to simultaneously describe
both A(2A)R versus A(3)R subtypes selectivity profile and the corresponding
receptor binding affinities. We have implemented an integrated application
of SVM-SVR approach, based on the use of our recently reported autocorrelated
molecular descriptors encoding for the Molecular Electrostatic Potential
(autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists
and to predict their binding affinity to the corresponding receptor
subtype of a large dataset of known pyrazolo-triazolo-pyrimidine
analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine
derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and
receptor binding affinity profiles.
Combining selectivity and affinity predictions using an integrated
Support Vector Machine (SVM) approach: An alternative tool to discriminate
between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine
antagonists binding sites
%0 Journal Article
%1 Michielan2009
%A Michielan, L.
%A Bolcato, C.
%A Federico, S.
%A Cacciari, B.
%A Bacilieri, M.
%A Klotz, K. N.
%A Kachler, S.
%A Pastorin, G.
%A Cardin, R.
%A Sperduti, A.
%A Spalluto, G.
%A Moro, S.
%D 2009
%J Bioorg Med Chem
%K & *Artificial A2A/*antagonists A3/*antagonists Adenosine Binding Chemical Discovery Drug Electricity Humans Intelligence Models, Protein Pyrazoles/chemical Pyrimidines/chemical Relationship Sites Static Structure-Activity Triazoles/chemical inhibitors/chemistry/*metabolism synthesis/*chemistry/*pharmacology synthesis/chemistry/pharmacology Receptor
%N 14
%P 5259-74
%T Combining selectivity and affinity predictions using an integrated
Support Vector Machine (SVM) approach: An alternative tool to discriminate
between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine
antagonists binding sites
%U http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19501513
%V 17
%X G Protein-coupled receptors (GPCRs) selectivity is an important aspect
of drug discovery process, and distinguishing between related receptor
subtypes is often the key to therapeutic success. Nowadays, very
few valuable computational tools are available for the prediction
of receptor subtypes selectivity. In the present study, we present
an alternative application of the Support Vector Machine (SVM) and
Support Vector Regression (SVR) methodologies to simultaneously describe
both A(2A)R versus A(3)R subtypes selectivity profile and the corresponding
receptor binding affinities. We have implemented an integrated application
of SVM-SVR approach, based on the use of our recently reported autocorrelated
molecular descriptors encoding for the Molecular Electrostatic Potential
(autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists
and to predict their binding affinity to the corresponding receptor
subtype of a large dataset of known pyrazolo-triazolo-pyrimidine
analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine
derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and
receptor binding affinity profiles.
@article{Michielan2009,
abstract = {G Protein-coupled receptors (GPCRs) selectivity is an important aspect
of drug discovery process, and distinguishing between related receptor
subtypes is often the key to therapeutic success. Nowadays, very
few valuable computational tools are available for the prediction
of receptor subtypes selectivity. In the present study, we present
an alternative application of the Support Vector Machine (SVM) and
Support Vector Regression (SVR) methodologies to simultaneously describe
both A(2A)R versus A(3)R subtypes selectivity profile and the corresponding
receptor binding affinities. We have implemented an integrated application
of SVM-SVR approach, based on the use of our recently reported autocorrelated
molecular descriptors encoding for the Molecular Electrostatic Potential
(autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists
and to predict their binding affinity to the corresponding receptor
subtype of a large dataset of known pyrazolo-triazolo-pyrimidine
analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine
derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and
receptor binding affinity profiles.},
added-at = {2010-12-14T18:12:02.000+0100},
author = {Michielan, L. and Bolcato, C. and Federico, S. and Cacciari, B. and Bacilieri, M. and Klotz, K. N. and Kachler, S. and Pastorin, G. and Cardin, R. and Sperduti, A. and Spalluto, G. and Moro, S.},
biburl = {https://www.bibsonomy.org/bibtex/22f8e4ed4db3710515a5188cd84bfc827/pharmawuerz},
endnotereftype = {Journal Article},
interhash = {4a33e3824eb1621911bd2a6e06321f66},
intrahash = {2f8e4ed4db3710515a5188cd84bfc827},
issn = {1464-3391 (Electronic) 1464-3391 (Linking)},
journal = {Bioorg Med Chem},
keywords = {& *Artificial A2A/*antagonists A3/*antagonists Adenosine Binding Chemical Discovery Drug Electricity Humans Intelligence Models, Protein Pyrazoles/chemical Pyrimidines/chemical Relationship Sites Static Structure-Activity Triazoles/chemical inhibitors/chemistry/*metabolism synthesis/*chemistry/*pharmacology synthesis/chemistry/pharmacology Receptor},
month = {Jul 15},
note = {Michielan, Lisa Bolcato, Chiara Federico, Stephanie Cacciari, Barbara
Bacilieri, Magdalena Klotz, Karl-Norbert Kachler, Sonja Pastorin,
Giorgia Cardin, Riccardo Sperduti, Alessandro Spalluto, Giampiero
Moro, Stefano Research Support, Non-U.S. Gov't England Bioorganic
\& medicinal chemistry Bioorg Med Chem. 2009 Jul 15;17(14):5259-74.
Epub 2009 May 21.},
number = 14,
pages = {5259-74},
shorttitle = {Combining selectivity and affinity predictions using an integrated
Support Vector Machine (SVM) approach: An alternative tool to discriminate
between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine
antagonists binding sites},
timestamp = {2010-12-14T18:20:21.000+0100},
title = {Combining selectivity and affinity predictions using an integrated
Support Vector Machine (SVM) approach: An alternative tool to discriminate
between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine
antagonists binding sites},
url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19501513},
volume = 17,
year = 2009
}