Kinases remain one of the major attractive therapeutic targets for a large number of
indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and
development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and
successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the
development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs
to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms.
The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to
work. Every year newer and better versions of these agents are introduced in the clinic. Several
computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their
potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge,
e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It
is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition
to the conventional strategies, modern technologies (machine learning, deep learning, artificial
intelligence, etc.) started yielding the results and building success stories. Computational tools
invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field.
The present review summarized the progress in utilizing computational methods and tools for
discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019).
Representative investigations have been discussed, while others are merely listed. The author believes
that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the
progress made so far and the future prospects of the field.