Improving the Reliability of Molecular String Representations for Generative Chemistry
Résumé
Generative modeling for chemistry has advanced rapidly in recent years, but this surge in popularity raises a foundational question: which molecular representation is best suited for modern machine learning models? Despite not being designed for generative tasks, SMILES remain the most commonly used string-based representation. However, while SMILES follow strict syntactic rules, grammatically correct SMILES strings do not always correspond to valid molecules. SELFIES were introduced as an alternative that addresses this limitation by ensuring that every string of SELFIES tokens represents to a valid molecule. In this study, we comprehensively evaluate the limitations of both SMILES and SELFIES as representations for generative models. We define two key criteria for robust molecular generation: viability, generated strings represent novel, unique molecules with correct valence, and fidelity, the distribution of physicochemical properties from sampled molecules resembles that of the training data. We find that approximately onefifth of molecules generated using canonical SMILES are invalid, failing the viability.
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