Improving Chemical Understanding of LLMs via SMILES Parsing

2025-05-22

AI Summary

Large language models (LLMs) exhibit weak comprehension of molecular structures encoded in SMILES, struggling even with fundamental tasks such as ring counting—severely limiting their utility in molecular science. To address this, we propose CLEANMOL, the first framework that establishes a hierarchical SMILES parsing task suite explicitly grounded in molecular graph structure. It decomposes molecular understanding into well-defined, graph-level subtasks: subgraph identification, ring detection, atomic connectivity verification, and global graph matching. CLEANMOL innovatively introduces an adaptive difficulty-scoring mechanism to curate a molecule-centric pretraining dataset and incorporates graph-structural priors to design chemistry-aware supervision signals. After fine-tuning on open-source LLMs, CLEANMOL achieves substantial improvements on foundational tasks—including ring counting—and attains state-of-the-art performance on the Mol-Instructions benchmark.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• LLMs struggle to interpret SMILES molecular representations
• Current models fail basic tasks like counting molecular rings
• Need for improved graph-level molecular comprehension in LLMs

Innovation

• Formulates SMILES parsing into clean deterministic tasks
• Introduces adaptive difficulty scoring for pretraining dataset
• Enhances structural comprehension via graph-level supervision