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2D-QSAR Modeling and Molecular Docking Studies on 1H-Pyrazole-1-carbothioamide Derivatives as EGFR Kinase Inhibitors
Author(s) -
Tawassl Tajelsir Hassan Hajalsiddig,
Abu Baker. M. Osman,
A. E. M. Saeed
Publication year - 2020
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.0c01323
Subject(s) - quantitative structure–activity relationship , partial least squares regression , erlotinib , pyrazole , egfr inhibitors , docking (animal) , chemistry , computational biology , epidermal growth factor receptor , pharmacology , stereochemistry , medicine , biochemistry , biology , computer science , receptor , machine learning , nursing
Epidermal growth factor receptor (EGFR) kinase has been commonly associated with cancers such as lung, ovarian, hormone-refractory prostate, metastatic colorectal, glioblastoma, pancreatic, and breast cancers. A series of 1 H -pyrazole-1-carbothioamide derivatives and their EGFR inhibitory activities were subjected to two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) studies. The 2D-QSAR models were constructed based on a forward selection of partial least-squares (PLS) and stepwise multiple linear regression (SW-MLR) methods validated by leave-one-out (LOO) and external test set prediction approaches. The stepwise multiple linear regression (SW-MLR) method presented an encouraging result as compared to other methods. The results of the study indicated that the activity of 1 H -pyrazole-1-carbothioamide derivatives as an EGFR kinase inhibitor was more influenced by adjacency distance matrix descriptors. The models were improved after outlier removal through the applicability domain. Based on the resultant models, 11 new compounds with high potency were designed as EGFR kinase inhibitors. Molecular docking studies were performed for designing compounds, and they were compared with erlotinib as a reference to predict their interactions in the active site and identify structural features necessary for producing biological activities.

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