Molecular de-novo design through deep reinforcement learning

• DOI: 10.1186/s13321-017-0235-x
• Publication Date: 2017-09-04T12:18:24Z
• Authors: Marcus Olivecrona; Thomas Blaschke; Ola Engkvist; Hongming Chen
• Keywords: FOS: Computer and information sciences; 0303 health sciences; Computer Science - Artificial Intelligence; Information technology; T58.5-58.64; Chemistry; 03 medical and health sciences; Artificial Intelligence (cs.AI); De novo design; Recurrent neural networks; Reinforcement learning; QD1-999; "Marie Sklodowska-Curie Actions" De novo design, Recurrent neural networks, Reinforcement learning; Research Article

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.