  Probabilistic generative deep learning for molecular design involves the
discovery and design of new molecules and analysis of their structure,
properties and activities by probabilistic generative models using the deep
learning approach. It leverages the existing huge databases and publications of
experimental results, and quantum-mechanical calculations, to learn and explore
molecular structure, properties and activities. We discuss the major components
of probabilistic generative deep learning for molecular design, which include
molecular structure, molecular representations, deep generative models,
molecular latent representations and latent space, molecular structure-property
and structure-activity relationships, molecular similarity and molecular
design. We highlight significant recent work using or applicable to this new
approach.

Machine Learning