  Drug development is an expensive and time-consuming process where thousands
of chemical compounds are being tested in order to find those possessing
drug-like properties while being safe and effective. One of key parts of the
early drug discovery process has become virtual drug screening -- a method used
to narrow down search for potential drugs by running computer simulations of
drug-target interactions. As these methods are known to demand huge amounts of
computational power to get accurate results, prediction models based on machine
learning techniques became a popular solution requiring less computational
power as well as offering the ability to generate novel chemical structures for
further research. Deep learning is to stay in drug discovery but has a long way
to go. Only in the past few years with increases in computing power have
researchers really started to embrace the potential of neural networks in
various stages of the drug discovery process. While prediction methods promise
great perspective in the future development of drug discovery they open new
questions and challenges that still have to be solved.
