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SELFIES and the future of molecular string representations.

Mario Krenn1, Qianxiang Ai2, Senja Barthel3

  • 1Max Planck Institute for the Science of Light (MPL), Erlangen, Germany.

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Artificial intelligence (AI) and machine learning (ML) in chemistry benefit from robust molecular string representations. The new SELF-referencing embedded string (Selfies) language offers a 100% robust alternative to older methods like Smiles for AI applications.

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Area of Science:

  • Chemistry and Materials Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly used in chemistry and materials science for tasks like property prediction and molecular design.
  • Traditional molecular string representations, such as Smiles, have limitations in AI/ML applications, often generating invalid chemical interpretations.
  • The development of robust molecular representations is crucial for advancing AI in these scientific fields.

Purpose of the Study:

  • To review the current landscape of molecular string representations for AI in chemistry.
  • To highlight the advantages of the SELF-referencing embedded string (Selfies) language over traditional methods.
  • To propose future research directions for robust molecular representations in AI-driven chemistry.

Main Methods:

  • The study is a perspective piece, analyzing existing literature and proposing future work.
  • It discusses the evolution and shortcomings of molecular string representations, focusing on Smiles and Selfies.
  • Future projects are conceptualized based on identified challenges and opportunities.

Main Results:

  • Selfies offers a 100% robust molecular string representation, overcoming the invalidity issues of Smiles.
  • This new language has already enabled new applications in chemistry.
  • The perspective outlines 16 concrete future projects for advancing robust molecular representations.

Conclusions:

  • Robust molecular string representations are essential for the future of AI in chemistry and materials science.
  • Selfies presents a significant advancement, offering greater reliability and enabling new possibilities.
  • Further research into new chemical domains, AI/language interfaces, and interpretability is recommended to fully exploit these representations.