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SELFIES and the future of molecular string representations

Authors :
Krenn, Mario
Ai, Qianxiang
Barthel, Senja
Carson, Nessa
Frei, Angelo
Frey, Nathan C.
Friederich, Pascal
Gaudin, Théophile
Gayle, Alberto Alexander
Jablonka, Kevin Maik
Lameiro, Rafael F.
Lemm, Dominik
Lo, Alston
Moosavi, Seyed Mohamad
Nápoles-Duarte, José Manuel
Nigam, AkshatKumar
Pollice, Robert
Rajan, Kohulan
Schatzschneider, Ulrich
Schwaller, Philippe
Skreta, Marta
Smit, Berend
Strieth-Kalthoff, Felix
Sun, Chong
Tom, Gary
von Rudorff, Guido Falk
Wang, Andrew
White, Andrew
Young, Adamo
Yu, Rose
Aspuru-Guzik, Alán
Source :
Cell Patterns 3(10), 100588(2022)
Publication Year :
2022

Abstract

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.<br />Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome!

Details

Database :
arXiv
Journal :
Cell Patterns 3(10), 100588(2022)
Publication Type :
Report
Accession number :
edsarx.2204.00056
Document Type :
Working Paper
Full Text :
https://doi.org/10.1016/j.patter.2022.100588