TY - JOUR AU - Olivecrona, Marcus AU - Blaschke, Thomas AU - Engkvist, Ola AU - Chen, Hongming PY - 2017 DA - 2017/09/04 TI - Molecular de-novo design through deep reinforcement learning JO - Journal of Cheminformatics SP - 48 VL - 9 IS - 1 AB - 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.Graphical abstract. SN - 1758-2946 UR - https://doi.org/10.1186/s13321-017-0235-x DO - 10.1186/s13321-017-0235-x ID - Olivecrona2017 ER -