• DOI:10.48550/arXiv.2211.13322
• Corpus ID: 254017556

Group SELFIES: A Robust Fragment-Based Molecular String Representation

@article{Cheng2022GroupSA,
  title={Group SELFIES: A Robust Fragment-Based Molecular String Representation},
  author={Austin H. Cheng and Andy Cai and Santiago Miret and Gustavo Malkomes and Mariano Phielipp and Al{\'a}n Aspuru-Guzik},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.13322}
}

• Austin H. Cheng, Andy Cai, +3 authors Alán Aspuru-Guzik
• Published 23 November 2022
• Biology
• ArXiv

We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees. Molecular string representations, such as SMILES and SELFIES, serve as the basis for molecular generation and optimization in chemical language models, deep generative models, and evolutionary methods. While SMILES and SELFIES leverage atomic representations, Group SELFIES builds on top of the chemical robustness guarantees of SELFIES by enabling group tokens, thereby creating additional flexibility to the representation. Moreover, the group tokens in Group SELFIES can take advantage of inductive biases of molecular fragments that capture meaningful chemical motifs. The advantages of capturing chemical motifs and flexibility are demonstrated in our experiments, which show that Group SELFIES improves distribution learning of common molecular datasets. Further experiments also show that random sampling of Group SELFIES strings improves the quality of generated molecules compared to regular SELFIES strings. Our open-source implementation of Group SELFIES is available at https://github.com/aspuru-guzik-group/group-selfies , which we hope will aid future research in molecular generation and optimization.