Recent advances in the self-referencing embedded strings (SELFIES) library

Alston Lo*, Robert Pollice, Akshat Kumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    1 Citation (Scopus)
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    String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencing embedded strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation called selfies. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints, and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfies, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfies (version 2.1.1) in this manuscript. Our library, selfies, is available at GitHub (

    Original languageEnglish
    Pages (from-to)897-908
    Number of pages12
    JournalDigital Discovery
    Issue number4
    Publication statusPublished - 1-Aug-2023

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