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AI for drug discovery has been a research hotspot in recent years, andSMILES-based language models has been increasingly applied in drug moleculardesign. However, no work has explored whether and how language modelsunderstand the chemical spatial structure from 1D sequences. In this work, wepre-train a transformer model on chemical language and fine-tune it toward drugdesign objectives, and investigate the correspondence between high-frequencySMILES substrings and molecular fragments. The results indicate that languagemodels can understand chemical structures from the perspective of molecularfragments, and the structural knowledge learned through fine-tuning isreflected in the high-frequency SMILES substrings generated by the model.

Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs