AI RESEARCH
From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models
arXiv CS.LG
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ArXi:2605.09949v1 Announce Type: new Understanding how chemical language models (CLMs) learn chemical meaning from molecular string representations, rather than only surface-level string patterns, is an important question in chemical representation learning and machine learning for chemistry. Chirality provides a demanding test case: enantiomers can differ greatly in pharmacological activity and toxicity, yet CLMs often struggle to distinguish chiral configurations reliably.