SELFIES and the future of molecular string representations. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., Lameiro, R. F., Lemm, D., Lo, A., Moosavi, S. M., Nápoles-Duarte, J. M., 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, A. D., Young, A., Yu, R., & Aspuru-Guzik, A. Patterns, 3(10):100588, October, 2022.
SELFIES and the future of molecular string representations [link]Paper  doi  abstract   bibtex   
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.
@article{krenn_selfies_2022,
	title = {{SELFIES} and the future of molecular string representations},
	volume = {3},
	issn = {2666-3899},
	url = {https://www.sciencedirect.com/science/article/pii/S2666389922002069},
	doi = {10.1016/j.patter.2022.100588},
	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.},
	language = {en},
	number = {10},
	urldate = {2022-10-31},
	journal = {Patterns},
	author = {Krenn, Mario and Ai, Qianxiang and Barthel, Senja and Carson, Nessa and Frei, Angelo and Frey, Nathan C. and Friederich, Pascal and Gaudin, Théophile and Gayle, Alberto Alexander and Jablonka, Kevin Maik and Lameiro, Rafael F. and Lemm, Dominik and Lo, Alston and Moosavi, Seyed Mohamad and Nápoles-Duarte, José Manuel and Nigam, AkshatKumar and Pollice, Robert and Rajan, Kohulan and Schatzschneider, Ulrich and Schwaller, Philippe and Skreta, Marta and Smit, Berend and Strieth-Kalthoff, Felix and Sun, Chong and Tom, Gary and Falk von Rudorff, Guido and Wang, Andrew and White, Andrew D. and Young, Adamo and Yu, Rose and Aspuru-Guzik, Alán},
	month = oct,
	year = {2022},
	pages = {100588},
}
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