Recent advances in the Self-Referencing Embedding Strings (SELFIES) library

Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn +1 more
2/7/2023
physics.chem-phcs.LG

No official code found. Best implementation: aspuru-guzik-group/selfies (864 stars, Apache-2.0, last commit 2025-05-17).

Abstract

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. 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 \selfieslib, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of \selfieslib (version 2.1.1) in this manuscript.

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Code Implementations(1)

aspuru-guzik-group/selfiesCommunity100%
Apache-2.0

Robust representation of semantically constrained graphs, in particular for molecules in chemistry

864146PythonMay 31, 20191 years agoApache-2.0

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Cite this paper

@article{lo2023recent,
  title  = {Recent advances in the Self-Referencing Embedding Strings (SELFIES) library},
  author = {Alston Lo and Robert Pollice and AkshatKumar Nigam and Andrew D. White and Mario Krenn and Alán Aspuru-Guzik},
  year   = {2023},
  eprint = {2302.03620},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.03620v1}
}

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