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

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This study introduces SELF-referencing embedded strings (SELFIES), a robust molecular representation that overcomes errors common in traditional methods like SMILES. The updated

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Area of Science:

  • Cheminformatics
  • Machine Learning in Chemistry
  • Computational Chemistry

Background:

  • String-based molecular representations are vital for cheminformatics.
  • Traditional representations like SMILES often produce syntactically and semantically incorrect outputs from generative models.
  • Deep learning adoption in chemistry necessitates robust molecular representations.

Purpose of the Study:

  • To present the current status of the selfies library (version 2.1.1).
  • To highlight advancements in the SELF-referencing embedded strings (SELFIES) representation.
  • To showcase improvements in robustness, efficiency, and features of the selfies library.

Main Methods:

  • Development and generalization of the SELFIES grammar.
  • Implementation of updated SELFIES representation in the open-source selfies library.
  • Enhancements in library design, efficiency, and feature support.

Main Results:

  • SELFIES provides an inherently 100% robust molecular representation.
  • The selfies library (v2.1.1) supports a wider range of molecules and semantic constraints.
  • Significant improvements in the efficiency and features of the selfies library have been achieved.

Conclusions:

  • The updated selfies library offers a robust and efficient solution for molecular representation in machine learning.
  • SELFIES effectively addresses the limitations of traditional string-based representations.
  • The open-source selfies library is a valuable tool for computational chemistry and cheminformatics research.