Exploring QSARs for inhibiting activity of epidermal growth factor receptor (EGFR) tyrosine kinase by MLR and PC-ANN

dc.contributor.authorManal Mohammad Wael Abdul Hafez Mohtaseb
dc.contributor.authorمنال محمد وائل عبد الحافظ المحتسب
dc.date.accessioned2022-11-08T07:05:06Z
dc.date.available2022-11-08T07:05:06Z
dc.date.issued2017-05-06
dc.description.abstractQuantitative structure- activity relationship (QSAR) study was preformed to understand the activity of a set of 113 compounds of Epidermal Growth Factor Receptor (EGFR) inhibitors. QSAR models were developed using multiple linear regression (MLR) as linear method. While principle component- artificial neural network (PC-ANN) modeling method was performed as nonlinear method. The MLR resulted with models (12-23) which have coefficient of determination (R2)>0.6, the best model (model 23) resulted with correlation coefficient (R) = 0.878, coefficient of determination (R2) =0.771, and adjusted coefficient of determination (R2adj) =0.719. Cross validation leave one out (LOO) and leave many out (LMO) were performed on the resulted MLR models, models 19-23 showed a good predictive power. After that principle component analysis (PCA) performed to divide the data into three data sets. Then the ANN performed on the chosen models (19-23) from leave one out (LOO) and leave many out (LMO) cross validation. ANN resulted models were validated through randomization test. The best ANN model with good predictive power was model 19 with R=0.812 for the test set.
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/6858
dc.language.isoen
dc.publisherAl-Quds University
dc.titleExploring QSARs for inhibiting activity of epidermal growth factor receptor (EGFR) tyrosine kinase by MLR and PC-ANN
dc.title.alternativeدراسة العلاقة الكمية بين الفعالية والصيغة البنائي بعض المركبات المثبطة للبروتين Epidermal Growth Factor Receptor (EGFR) باستخدام طريقتي PC- ANN وMLR
dc.typeThesis
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