Molecular de-novo design through deep reinforcement learning
Abstract 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 generat