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…

• A
  AstraZeneca
• EC
  European Commission

  Award: 676434
• RE
  Research Executive Agency
• HM
  H2020 Marie Skłodowska-Curie Actions

  Award: 676434