  Materials discovery is decisive for tackling urgent challenges related to
energy, the environment, health care and many others. In chemistry,
conventional methodologies for innovation usually rely on expensive and
incremental strategies to optimize properties from molecular structures. On the
other hand, inverse approaches map properties to structures, thus expediting
the design of novel useful compounds. In this chapter, we examine the way in
which current deep generative models are addressing the inverse chemical
discovery paradigm. We begin by revisiting early inverse design algorithms.
Then, we introduce generative models for molecular systems and categorize them
according to their architecture and molecular representation. Using this
classification, we review the evolution and performance of important molecular
generation schemes reported in the literature. Finally, we conclude
highlighting the prospects and challenges of generative models as cutting edge
tools in materials discovery.
