Marvels of Artificial and Computational Intelligence in Life Sciences

In silico Approaches to Tyrosine Kinase Inhibitors’ Development

Author(s): S. Sugunakala* and S. Selvaraj

Pp: 150-178 (29)

DOI: 10.2174/9789815136807123010014

* (Excluding Mailing and Handling)


Many cellular communications and cellular activities are regulated by a class of enzyme tyrosine kinases. Mutations or increased expression of these enzymes lead to many proliferative cancers as well as other non-proliferative diseases such as psoriasis, atherosclerosis and some inflammatory diseases. Hence, they are considered vital and prospective therapeutic targets. Over the past decade, considerable research work has been carried out to develop potential inhibitors against these tyrosine kinases. So far, a number of compounds have been identified successfully as tyrosine kinase inhibitors and many compounds were developed as drugs to treat tyrosine kinase-induced diseases. Behind the successful development of these inhibitors, many Computer Aided Drug Design (CADD) (in silico) approaches include molecular modelling, high throughput virtual screening against various chemical databases, and docking (both rigid and flexible method of docking). Further many studies identified the possible features which are responsible for tyrosine kinase inhibition activities for a number of series of compounds through the quantitative structure-activity/property relationship (QSAR/QSPR) process. In this review article, the structural characteristics, mechanism of action, and mode of inhibition of tyrosine kinases are discussed followed by the successful applications of a variety of in silico approaches in tyrosine kinase inhibitors development.

Keywords: Artificial intelligence, Computer-aided drug design, Computational intelligence, Docking, EGFR, In silico approaches, Ligand based drug design, Machine learning, Pharmacophore, Protein tyrosine kinases, QSAR, Structure based drug design, Tyrsoine kinase inhibitors (TKI), Virtual screening, VEGFR.

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