Molecular De Novo Design through Deep Reinforcement Learning
  This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn
to generate structures with certain specified desirable properties. We
demonstrate how this model can execute a range of tasks such as generating
analogues to a query structure and generating compounds predicted to be active
against a biological target. As a proof of principle, the model is first
trained to generate molecules that do not contain sulphur. As a second example,
the model is trained to generate analogues to the drug Celecoxib, a technique
that could be used for scaffold hopping or library expansion starting from a
single molecule. Finally, when tuning the model towards generating compounds
predicted to be active against the dopamine receptor type 2, the model
generates structures of which more than 95% are predicted to be active,
including experimentally confirmed actives that have not been included in
either the generative model nor the activity prediction model.
