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SELFIES and the future of molecular string representations.

Authors :
Krenn M
Ai Q
Barthel S
Carson N
Frei A
Frey NC
Friederich P
Gaudin T
Gayle AA
Jablonka KM
Lameiro RF
Lemm D
Lo A
Moosavi SM
NĂ¡poles-Duarte JM
Nigam A
Pollice R
Rajan K
Schatzschneider U
Schwaller P
Skreta M
Smit B
Strieth-Kalthoff F
Sun C
Tom G
Falk von Rudorff G
Wang A
White AD
Young A
Yu R
Aspuru-Guzik A
Source :
Patterns (New York, N.Y.) [Patterns (N Y)] 2022 Oct 14; Vol. 3 (10), pp. 100588. Date of Electronic Publication: 2022 Oct 14 (Print Publication: 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: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, 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 /> (© 2022 The Author(s).)

Details

Language :
English
ISSN :
2666-3899
Volume :
3
Issue :
10
Database :
MEDLINE
Journal :
Patterns (New York, N.Y.)
Publication Type :
Academic Journal
Accession number :
36277819
Full Text :
https://doi.org/10.1016/j.patter.2022.100588