MoleCode unlocks structural intelligence in large language models
Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses atoms, bonds, branches and rings into a compact sequence in which topology is implicit, forcing LLMs to reconstruct molecular structure before performing the requested chemical operation. Here we introduce MoleCode, an LLM-native, training-free, graph-explicit molecular language in which all molecular components are represented as typed entities with persistent identifiers and explicit relations. MoleCode makes molecular topology directly readable, editable and auditable within the language context, allowing an ...
| Authors | Zhiyuan Yan, Chen Liu, Boxuan Zhao et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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