Generative Models for Automatic Chemical Design
This is an incremental review chapter for researchers in materials science and chemistry, summarizing existing methods without introducing new techniques.
The paper reviews how deep generative models address the inverse chemical design paradigm, mapping properties to structures to expedite the discovery of novel compounds, and categorizes models by architecture and representation while highlighting their evolution and performance.
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.