  In the space of only a few years, deep generative modeling has revolutionized
how we think of artificial creativity, yielding autonomous systems which
produce original images, music, and text. Inspired by these successes,
researchers are now applying deep generative modeling techniques to the
generation and optimization of molecules - in our review we found 45 papers on
the subject published in the past two years. These works point to a future
where such systems will be used to generate lead molecules, greatly reducing
resources spent downstream synthesizing and characterizing bad leads in the
lab. In this review we survey the increasingly complex landscape of models and
representation schemes that have been proposed. The four classes of techniques
we describe are recursive neural networks, autoencoders, generative adversarial
networks, and reinforcement learning. After first discussing some of the
mathematical fundamentals of each technique, we draw high level connections and
comparisons with other techniques and expose the pros and cons of each. Several
important high level themes emerge as a result of this work, including the
shift away from the SMILES string representation of molecules towards more
sophisticated representations such as graph grammars and 3D representations,
the importance of reward function design, the need for better standards for
benchmarking and testing, and the benefits of adversarial training and
reinforcement learning over maximum likelihood based training.
