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 active... https://www.jmannino.com/hot-offer-University-of-Tennessee-Knoxville-Collegiate-Text-Galaxy-S23-Clear-Case-p107375-amazing-offer/