GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition

• URL: http://arxiv.org/abs/2506.07553v2
• Date: Tue, 10 Jun 2025 02:18:38 GMT
• Title: GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition
• Authors: Jingchao Wang, Haote Yang, Jiang Wu, Yifan He, Xingjian Wei, Yinfan Wang, Chengjin Liu, Lingli Ge, Lijun Wu, Bin Wang, Dahua Lin, Conghui He,
• Abstract summary: GTR-Mol-VLM is a novel framework featuring two key innovations.<n>It emulates human reasoning by incrementally parsing molecular graphs through sequential atom-bond predictions.<n>MolRec-Bench is the first benchmark designed for a fine-grained evaluation of graph-parsing accuracy in OCSR.
• Score: 60.76623665324548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical Chemical Structure Recognition (OCSR) is crucial for digitizing chemical knowledge by converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown potential in this task, their image-captioning approach often struggles with complex molecular structures and inconsistent annotations. To overcome these challenges, we introduce GTR-Mol-VLM, a novel framework featuring two key innovations: (1) the Graph Traversal as Visual Chain of Thought mechanism that emulates human reasoning by incrementally parsing molecular graphs through sequential atom-bond predictions, and (2) the data-centric principle of Faithfully Recognize What You've Seen, which addresses the mismatch between abbreviated structures in images and their expanded annotations. To support model development, we constructed GTR-CoT-1.3M, a large-scale instruction-tuning dataset with meticulously corrected annotations, and introduced MolRec-Bench, the first benchmark designed for a fine-grained evaluation of graph-parsing accuracy in OCSR. Comprehensive experiments demonstrate that GTR-Mol-VLM achieves superior results compared to specialist models, chemistry-domain VLMs, and commercial general-purpose VLMs. Notably, in scenarios involving molecular images with functional group abbreviations, GTR-Mol-VLM outperforms the second-best baseline by approximately 14 percentage points, both in SMILES-based and graph-based metrics. We hope that this work will drive OCSR technology to more effectively meet real-world needs, thereby advancing the fields of cheminformatics and AI for Science. We will release GTR-CoT at https://github.com/opendatalab/GTR-CoT.

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