文章基本信息

标题：Molecular de-novo design through deep reinforcement learning
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作者：Marcus Olivecrona ; Thomas Blaschke ; Ola Engkvist 等
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2017
卷号：9
期号：1
页码：48
DOI：10.1186/s13321-017-0235-x
语种：English
出版社：BioMed Central
摘要：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 .
关键词：De novo design ; Recurrent neural networks ; Reinforcement learning