Abstract
A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.
Highlights
Src (c-src, pp60-src, or p60-src) is a nonreceptor, cytoplasmic tyrosine kinase, the first of its kind to be discovered in the living world, whereas the corresponding gene was the first oncogene to be uncovered [1]
Nonclinical evidence has pointed to the inhibition of src kinases as a possible method of therapy for the pulmonary vascular remodeling and right ventricular hypertrophy in pulmonary hypertension [4], several reports indicate that dual Abl/src inhibitor dasatinib may induce pulmonary hypertension [5,6,7]; it was more recently suggested that this dasatinib effect may be independent of the src inhibition [7]
We developed a set of quantitative structure-activity relationship (QSAR) models with different descriptors and machine learning classification algorithms, integrated by stacking, to be used for virtual screening of c-src tyrosin kinase inhibitors
Summary
Src (c-src, pp60-src, or p60-src) is a nonreceptor, cytoplasmic tyrosine kinase, the first of its kind to be discovered (in the 1970s) in the living world, whereas the corresponding gene was the first oncogene to be uncovered [1]. Nonclinical evidence has pointed to the inhibition of src kinases as a possible method of therapy for the pulmonary vascular remodeling and right ventricular hypertrophy in pulmonary hypertension [4], several reports indicate that dual Abl/src inhibitor dasatinib may induce pulmonary hypertension [5,6,7]; it was more recently suggested that this dasatinib effect may be independent of the src inhibition [7]. A constant interest for understanding the pharmacology of this class of compounds, as well as for developing new src inhibitors, may open the doors wide for multiple therapeutic applications for these inhibitors in a variety of pathologies
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