Molecular de-novo design through deep reinforcement learning

Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen

Open source

DOI: 10.1186/s13321-017-0235-x
Published: 2017-09-04
Container: Journal of Cheminformatics
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1186/s13321-017-0235-x,
  title = {Molecular de-novo design through deep reinforcement learning},
  author = {Marcus Olivecrona and Thomas Blaschke and Ola Engkvist and Hongming Chen},
  year = {2017},
  journal = {Journal of Cheminformatics},
  doi = {10.1186/s13321-017-0235-x},
  url = {https://doi.org/10.1186/s13321-017-0235-x}
}

RIS

TY  - JOUR
TI  - Molecular de-novo design through deep reinforcement learning
AU  - Marcus Olivecrona
AU  - Thomas Blaschke
AU  - Ola Engkvist
AU  - Hongming Chen
PY  - 2017
JO  - Journal of Cheminformatics
DO  - 10.1186/s13321-017-0235-x
UR  - https://doi.org/10.1186/s13321-017-0235-x
ER  -

APA

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017). Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics. https://doi.org/10.1186/s13321-017-0235-x

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crossref · retrieved 2026-09-27T05:03:41.006Z