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 generating compounds predicted to be silver condenser tumble dryer 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, spoon rsx 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.