Ouadah K.Tchouar N.Belaidi S.Oukil O.Cinar M.20.04.20192019-04-2020.04.20192019-04-2020181557-7910https://dx.doi.org/10.1166/jbns.2018.1513https://hdl.handle.net/20.500.12403/396Series of twenty-four compounds was the field for applying multi-parameter optimization (MPO) methods and qualitative approximations of structural activity relationships in order to determine the role of various physico-chemical and quantum descriptors used in QSAR modeling as independent variables. Anaplastic lymphoma kinase (ALK) inhibitory activity was considered as a component in this study. In order to illustrate the quantitative relationships between the molecular descriptors and the activity of pyrazolone derivatives, we have applied a multiple linear regression (MLR) procedure. By adopting cross-validation with the leave-one-out method, we could estimate the predictability of models. Our results suggest a QSAR model based on the following descriptors: S, V, LogP, POL, Ref, HOMO, LUMO, E, , HBA, PSA and NRB, for the inhibitory activities against ALK. Results show high correlation between experimental and predicted activity values, indicating the validation and the good quality of the QSAR model. Copyright © 2018 American Scientific Publishers All rights reserved.eninfo:eu-repo/semantics/closedAccessALKMLRPyrazoloneQSARSAR/SPREnzymesLinear regressionMolecular graphicsOncologyAnaplastic lymphoma kinaseLeave one out methodsMulti-parameter optimizationsMultiple linear regressionsPyrazolonesQSARQuantitative structuresSAR/SPRComputational chemistryALKMLRPyrazoloneQSARSAR/SPREnzymesLinear regressionMolecular graphicsOncologyAnaplastic lymphoma kinaseLeave one out methodsMulti-parameter optimizationsMultiple linear regressionsPyrazolonesQSARQuantitative structuresSAR/SPRComputational chemistryArticle12225025910.1166/jbns.2018.15132-s2.0-85049147588N/A