  Deep learning bears promise for drug discovery, including advanced image
analysis, prediction of molecular structure and function, and automated
generation of innovative chemical entities with bespoke properties. Despite the
growing number of successful prospective applications, the underlying
mathematical models often remain elusive to interpretation by the human mind.
There is a demand for 'explainable' deep learning methods to address the need
for a new narrative of the machine language of the molecular sciences. This
review summarizes the most prominent algorithmic concepts of explainable
artificial intelligence, and dares a forecast of the future opportunities,
potential applications, and remaining challenges.
