title: Improving Chemical Understanding of LLMs via SMILES Parsing
summary: Large language models (LLMs) are increasingly recognized as powerful tools
  for scientific discovery, particularly in molecular science. A fundamental
  requirement for these models is the ability to accurately understand molecular
  structures, commonly encoded in the SMILES representation. However, current
  LLMs struggle to interpret SMILES, even failing to carry out basic tasks such
  as counting molecular rings. To address this limitation, we introduce CLEANMOL,
  a novel framework that formulates SMILES parsing into a suite of clean and
  deterministic tasks explicitly designed to promote graph-level molecular
  comprehension. These tasks span from subgraph matching to global graph
  matching, providing structured supervision aligned with molecular structural
  properties. We construct a molecular pretraining dataset with adaptive
  difficulty scoring and pre-train open-source LLMs on these tasks. Our results
  show that CLEANMOL not only enhances structural comprehension but also achieves
  the best or competes with the baseline on the Mol-Instructions benchmark.
paper_authors._id: 683690c131bd3eb4a8958b02
paper_authors.name: Yunhui Jang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:56:50.299Z
paper_authors._id: 668785136c2f7efac100ffba
paper_authors.avatarUrl: /avatars/8dd6fe11c8a2f6da5b2486b8381944f0.svg
paper_authors.fullname: Yunhui Jang
paper_authors.type: user
paper_authors.user: yunhuijang
paper_authors._id: 683690c131bd3eb4a8958b03
paper_authors.name: Jaehyung Kim
paper_authors._id: 683690c131bd3eb4a8958b04
paper_authors.name: Sungsoo Ahn
paper_title: Improving Chemical Understanding of LLMs via SMILES Parsing
paper_summary: Large language models (LLMs) are increasingly recognized as powerful tools
  for scientific discovery, particularly in molecular science. A fundamental
  requirement for these models is the ability to accurately understand molecular
  structures, commonly encoded in the SMILES representation. However, current
  LLMs struggle to interpret SMILES, even failing to carry out basic tasks such
  as counting molecular rings. To address this limitation, we introduce CLEANMOL,
  a novel framework that formulates SMILES parsing into a suite of clean and
  deterministic tasks explicitly designed to promote graph-level molecular
  comprehension. These tasks span from subgraph matching to global graph
  matching, providing structured supervision aligned with molecular structural
  properties. We construct a molecular pretraining dataset with adaptive
  difficulty scoring and pre-train open-source LLMs on these tasks. Our results
  show that CLEANMOL not only enhances structural comprehension but also achieves
  the best or competes with the baseline on the Mol-Instructions benchmark.
paper_ai_summary: CLEANMOL, a novel framework, enhances structural comprehension in large language models for molecular science by formulating SMILES parsing into structured tasks, improving performance on Mol-Instructions.
paper_ai_keywords: large language models SMILES representation mol-instructions CLEANMOL subgraph matching global graph matching molecular pretraining graph-level molecular comprehension adaptive difficulty scoring