Computational Approaches for the Identification and Optimization of Src Family Kinases Inhibitors

Src family kinases (SFKs) are a group of non-receptor tyrosine kinases whose activity is involved in the regulation of cellular morphology, motility, proliferation and survival. An aberrant activation and expression of these kinases contribute to the pathogenesis and progression of a broad range of diseases, such as a large number of solid tumors, various hematological malignancies and some neuronal pathologies. The search for SFK inhibitors is therefore a promising research topic in medicinal chemistry. Computational studies such as receptor-based and/or ligand-based virtual screening, docking, and molecular modeling proved to be a powerful tool for identifying new SFKs inhibitors. In this review we report and analyze the main examples of computational approaches that allowed the identification of new SFKs ligands and the optimization of either activity and pharmacokinetic profile of lead compounds.