Rethinking Molecular Text Representations for LLMs: An Empirical Study

arXiv:2606.03057v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a systematic benchmark evaluating LLM molecular competence across nine representations and eight chemical tasks. We benchmark 16 LLMs across five model families, including reasoning and non-reasoning variants, chemistry-specialized LLMs, and closed frontier models. Performance is strongly representation-dependent and no single representation wins across tasks, though CML is the best, followed by MolJSON,

Source: arXiv cs.LG — read the full report at the original publisher.

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