Deep Learning in Cheminformatics

Deep Learning (DL) is among the most promising modeling techniques to tackle hard Cheminformatics problems, whose solutions continue to have profound humanitarian and societal implications. This chapter covers two relevant applications of deep learning in the Cheminformatics landscape. The first is QSAR/QSPR analysis, which concerns the prediction of chemical properties or biological activity of molecular structures. Along with a review of the main results obtained through the use of deep learning in the field, we provide an overview of neural network models capable of processing structured data, such as sequences, trees, and graphs, which allow one to represent the rich structure of chemical data for predictive purposes. The second is de novo drug design, which concerns the generation of novel molecular structures with desired chemical properties to speed up the drug discovery pipeline. Here, we present the family of deep generative models for molecule generation, by which it is possible to learn the distribution of molecules from data and to generate novel chemical structures through sampling.