Exploring chemical space — Generative models and their evaluation

Abstract

Recent advances in the field of artificial intelligence, specifically regarding deep learning methods, have invigorated research into novel ways for the exploration of chemical space. Compared to more traditional methods that rely on chemical fragments and combinatorial recombination deep generative models generate molecules in a non-transparent way that defies easy rationalization. However, this opaque nature also promises to explore uncharted chemical space in novel ways that do not rely on structural similarity directly. These aspects and the complexity of training such models makes model assessment regarding novelty, uniqueness, and distribution of generated molecules a central aspect. This perspective gives an overview of current methodologies for chemical space exploration with an emphasis on deep neural network approaches. Key aspects of generative models include choice of molecular representation, the targeted chemical space, and the methodology for assessing and validating chemical space coverage.