  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 online, which we hope will aid
future research in molecular generation and optimization.
