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<p>We investigate the ability of vision–language models (VLMs) to extract structural region definitions from rasterized multiple sequence alignment (MSA) images, as part of the PDB-Descriptome project. Using synthetic MSAs with annotated structural biological entities (SBEs) and structural biological Referring Expressions (SBREs), we evaluate two VLMs, gemini-2.5-flash and gemini-2.5-pro, under naïve and strict prompts. While VLMs perform well in SBRE extraction, they show poor accuracy in defining SBE boundaries. In contrast, a human annotator achieves high boundary precision with slightly lower textual accuracy. These results support a human-in-the-loop pipeline for reliable structure–text annotation.</p>

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VLMs struggle to extract definitions of structural segments from MSA images: toward the design of a human-in-the-loop annotation pipeline for the PDB-Descriptome project

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  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Koya Sakuma

摘要

We investigate the ability of vision–language models (VLMs) to extract structural region definitions from rasterized multiple sequence alignment (MSA) images, as part of the PDB-Descriptome project. Using synthetic MSAs with annotated structural biological entities (SBEs) and structural biological Referring Expressions (SBREs), we evaluate two VLMs, gemini-2.5-flash and gemini-2.5-pro, under naïve and strict prompts. While VLMs perform well in SBRE extraction, they show poor accuracy in defining SBE boundaries. In contrast, a human annotator achieves high boundary precision with slightly lower textual accuracy. These results support a human-in-the-loop pipeline for reliable structure–text annotation.