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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

Code availability

The code used in this paper is available at


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

Author information




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.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

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).

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