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Molecular de-novo design through deep reinforcement learning
Open accessAbstract
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 .
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Metadata
- Authors
- Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H.
- Venue
- Journal of Cheminformatics
- Published
- 2017-09-04
- Volume
- 9
- Issue
- 1
- Pages
- 48-48
- ISSN
- 1758-2946 (ISSN-L)
- OpenAlex
- W2610148085
- Cited by
- 1,324
- References
- 9
- FWCI
- 56.24
- Type
- journal article
- Language
- en
- Open access
- gold
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