Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase
| Autor: | Bogdan Ionel Tamba, Mihaela Dinu, Robert Ancuceanu, Cristina Silvia Stoicescu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Quantitative structure–activity relationship Molecular Conformation Quantitative Structure-Activity Relationship Computational biology Molecular Dynamics Simulation Catalysis Article drug discovery Inorganic Chemistry 03 medical and health sciences molecular descriptors 0302 clinical medicine Molecular descriptor Humans cancer Physical and Theoretical Chemistry Molecular Biology Protein Kinase Inhibitors Spectroscopy Virtual screening Chemistry Drug discovery QSAR Organic Chemistry other Reproducibility of Results General Medicine molecular docking Models Theoretical chEMBL virtual screening Computer Science Applications Molecular Docking Simulation c-src-tyrosine kinase 030104 developmental biology src-Family Kinases Docking (molecular) 030220 oncology & carcinogenesis Proto-oncogene tyrosine-protein kinase Src Applicability domain Protein Binding |
| Zdroj: | International Journal of Molecular Sciences Volume 21 Issue 1 |
| DOI: | 10.0113/v2 |
| Popis: | Prototype of a family of at least nine members, c-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 have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 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 Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target. |
| Databáze: | OpenAIRE |
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