The Experts below are selected from a list of 744 Experts worldwide ranked by ideXlab platform
Lisa Michielan - One of the best experts on this subject based on the ideXlab platform.
-
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.
Bioorganic & medicinal chemistry, 2009Co-Authors: Lisa Michielan, Magdalena Bacilieri, Chiara Bolcato, Giorgia Pastorin, Barbara Cacciari, Karlnorbert Klotz, Stephanie Federico, Sonja Kachler, Riccardo Cardin, Alessandro SperdutiAbstract: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.
-
linear and nonlinear 3d qsar approaches in tandem with ligand based homology modeling as a computational strategy to depict the pyrazolo triazolo Pyrimidine Antagonists binding site of the human adenosine a2a receptor
Journal of Chemical Information and Modeling, 2008Co-Authors: Lisa Michielan, Magdalena Bacilieri, Andrea Schiesaro, Chiara Bolcato, Giorgia Pastorin, Giampiero Spalluto, Barbara Cacciari, Karlnorbert Klotz, Chosei Kaseda, Stefano MoroAbstract:The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor Antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A Antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rho...
Magdalena Bacilieri - One of the best experts on this subject based on the ideXlab platform.
-
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.
Bioorganic & medicinal chemistry, 2009Co-Authors: Lisa Michielan, Magdalena Bacilieri, Chiara Bolcato, Giorgia Pastorin, Barbara Cacciari, Karlnorbert Klotz, Stephanie Federico, Sonja Kachler, Riccardo Cardin, Alessandro SperdutiAbstract: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.
-
linear and nonlinear 3d qsar approaches in tandem with ligand based homology modeling as a computational strategy to depict the pyrazolo triazolo Pyrimidine Antagonists binding site of the human adenosine a2a receptor
Journal of Chemical Information and Modeling, 2008Co-Authors: Lisa Michielan, Magdalena Bacilieri, Andrea Schiesaro, Chiara Bolcato, Giorgia Pastorin, Giampiero Spalluto, Barbara Cacciari, Karlnorbert Klotz, Chosei Kaseda, Stefano MoroAbstract:The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor Antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A Antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rho...
Chiara Bolcato - One of the best experts on this subject based on the ideXlab platform.
-
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.
Bioorganic & medicinal chemistry, 2009Co-Authors: Lisa Michielan, Magdalena Bacilieri, Chiara Bolcato, Giorgia Pastorin, Barbara Cacciari, Karlnorbert Klotz, Stephanie Federico, Sonja Kachler, Riccardo Cardin, Alessandro SperdutiAbstract: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.
-
linear and nonlinear 3d qsar approaches in tandem with ligand based homology modeling as a computational strategy to depict the pyrazolo triazolo Pyrimidine Antagonists binding site of the human adenosine a2a receptor
Journal of Chemical Information and Modeling, 2008Co-Authors: Lisa Michielan, Magdalena Bacilieri, Andrea Schiesaro, Chiara Bolcato, Giorgia Pastorin, Giampiero Spalluto, Barbara Cacciari, Karlnorbert Klotz, Chosei Kaseda, Stefano MoroAbstract:The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor Antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A Antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rho...
Giorgia Pastorin - One of the best experts on this subject based on the ideXlab platform.
-
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.
Bioorganic & medicinal chemistry, 2009Co-Authors: Lisa Michielan, Magdalena Bacilieri, Chiara Bolcato, Giorgia Pastorin, Barbara Cacciari, Karlnorbert Klotz, Stephanie Federico, Sonja Kachler, Riccardo Cardin, Alessandro SperdutiAbstract: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.
-
linear and nonlinear 3d qsar approaches in tandem with ligand based homology modeling as a computational strategy to depict the pyrazolo triazolo Pyrimidine Antagonists binding site of the human adenosine a2a receptor
Journal of Chemical Information and Modeling, 2008Co-Authors: Lisa Michielan, Magdalena Bacilieri, Andrea Schiesaro, Chiara Bolcato, Giorgia Pastorin, Giampiero Spalluto, Barbara Cacciari, Karlnorbert Klotz, Chosei Kaseda, Stefano MoroAbstract:The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor Antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A Antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rho...
Barbara Cacciari - One of the best experts on this subject based on the ideXlab platform.
-
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.
Bioorganic & medicinal chemistry, 2009Co-Authors: Lisa Michielan, Magdalena Bacilieri, Chiara Bolcato, Giorgia Pastorin, Barbara Cacciari, Karlnorbert Klotz, Stephanie Federico, Sonja Kachler, Riccardo Cardin, Alessandro SperdutiAbstract: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.
-
linear and nonlinear 3d qsar approaches in tandem with ligand based homology modeling as a computational strategy to depict the pyrazolo triazolo Pyrimidine Antagonists binding site of the human adenosine a2a receptor
Journal of Chemical Information and Modeling, 2008Co-Authors: Lisa Michielan, Magdalena Bacilieri, Andrea Schiesaro, Chiara Bolcato, Giorgia Pastorin, Giampiero Spalluto, Barbara Cacciari, Karlnorbert Klotz, Chosei Kaseda, Stefano MoroAbstract:The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor Antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A Antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rho...
