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.
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.
authors.affiliation: Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy.
authors.forename: Lisa
authors.initials: L
authors.lastname: Michielan
authors.forename: Chiara
authors.initials: C
authors.lastname: Bolcato
authors.forename: Stephanie
authors.initials: S
authors.lastname: Federico
authors.forename: Barbara
authors.initials: B
authors.lastname: Cacciari
authors.forename: Magdalena
authors.initials: M
authors.lastname: Bacilieri
authors.forename: Karl-Norbert
authors.initials: KN
authors.lastname: Klotz
authors.forename: Sonja
authors.initials: S
authors.lastname: Kachler
authors.forename: Giorgia
authors.initials: G
authors.lastname: Pastorin
authors.forename: Riccardo
authors.initials: R
authors.lastname: Cardin
authors.forename: Alessandro
authors.initials: A
authors.lastname: Sperduti
authors.forename: Giampiero
authors.initials: G
authors.lastname: Spalluto
authors.forename: Stefano
authors.initials: S
authors.lastname: Moro
mesh_terms: D058917:Adenosine A2 Receptor Antagonists; D058918:Adenosine A3 Receptor Antagonists; D001185:Artificial Intelligence; D001665:Binding Sites; D055808:Drug Discovery; D006801:Humans; D008956:Models, Chemical; D011485:Protein Binding; D011720:Pyrazoles / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology; D011743:Pyrimidines / Q000138:chemical synthesis / Q000737:chemistry* / Q000494:pharmacology*; D043705:Receptor, Adenosine A2A / Q000737:chemistry / Q000378:metabolism*; D043684:Receptor, Adenosine A3 / Q000737:chemistry / Q000378:metabolism*; D055672:Static Electricity; D013329:Structure-Activity Relationship; D014230:Triazoles / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology
chemical_list: D058917:Adenosine A2 Receptor Antagonists; D058918:Adenosine A3 Receptor Antagonists; D011720:Pyrazoles; D011743:Pyrimidines; D043705:Receptor, Adenosine A2A; D043684:Receptor, Adenosine A3; D014230:Triazoles; C031280:pyrazole