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. 2020 Nov 4;11:5575. doi: 10.1038/s41467-020-19266-y

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — The transformer memorized the target sequences if the target sequences were all canonical SMILES (red dots). It also reasonably predicted the sequence composition for randomized target SMILES (cyan rectangle, dashed) but its performance decreased for prediction of exact full SMILES (cyan circle). The performance normalized by the percentage of canonical sequences increased with the number of augmentations, N, since some of the random sequences were canonical ones.