A Novel QSAR Model for Evaluating and Predicting the Inhibition Activity of Dipeptidyl Aspartyl Fluoromethylketones
Antreas AfantitisDepartment of ChemoInformatics, NovaMechanics Ltd., CyprusGeorgia MelagrakiSchool of Chemical Engineering, National Technical University of Athens, Athens, GreeceHaralambos SarimveisSchool of Chemical Engineering, National Technical University of Athens, Athens, GreecePanayiotis A. KoutentisDepartment of Chemistry, University of Cyprus, P. O. Box 20537, 1678 Nicosia, CyprusJohn MarkopoulosDepartment of Chemistry, University of Athens, Athens, GreeceOlga Igglessi‐MarkopoulouSchool of Chemical Engineering, National Technical University of Athens, Athens, Greece
2006en
ABI
Annotatsiya
Abstract A linear quantitative structure activity relationship model is obtained using Multiple Linear Regression (MLR) analysis as applied to a series of 49 dipeptidyl aspartyl fluoromethylketone derivatives with inhibitory activity of the caspase enzyme. For the selection of the best descriptors, the elimination selection stepwise regression method is utilized. The accuracy of the proposed MLR model is illustrated using the following evaluation techniques: cross validation, validation through an external test set, and Y‐randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.
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