A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.
A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.
Afantitis
Antreas
A
Biomedical Sciences Research Center Alexander Fleming, Athens, Greece. afantitis@fleming.gr
Melagraki
Georgia
G
Sarimveis
Haralambos
H
Koutentis
Panayiotis A
PA
Igglessi-Markopoulou
Olga
O
Kollias
George
G
D000465:Algorithms; D020128:Inhibitory Concentration 50; D016018:Least-Squares Analysis; D016014:Linear Models; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D052999:Quinazolinones / Q000737:chemistry / Q000494:pharmacology*; D054367:Receptors, CXCR3 / Q000037:antagonists & inhibitors* / Q000737:chemistry; D015203:Reproducibility of Results
D052999:Quinazolinones; D054367:Receptors, CXCR3