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Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks

The Original Article was published on 18 May 2020

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Fig. 1: Repurposing cRNN generates novel OPMs.
Fig. 2: Benchmarking cRNN models against a GB-GA baseline.

Data availability

The chemical structures and labels used for training and validation of the supervised and unsupervised models, with the exception of 684 proprietary molecules, are available at https://github.com/learningmatter-mit/Deep-Drug-Coder20.

Code availability

The code used in this paper is available at https://github.com/learningmatter-mit/Deep-Drug-Coder.

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Acknowledgements

We acknowledge Sumitomo Chemical for providing financial support for this work.

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R.G.-B. supervised the research, and planned the project with contributions from S.M. S.M. trained and analysed the machine learning models. T.Y. ran the DFT calculations with contributions from R.G.-B. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Rafael Gómez-Bombarelli.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Olexandr Isayev, Connor Coley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Discussion Sections 1–8, Figs. 1–3 and Tables 1–5.

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Mohapatra, S., Yang, T. & Gómez-Bombarelli, R. Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks. Nat Mach Intell 2, 749–752 (2020). https://doi.org/10.1038/s42256-020-00268-w

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