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<p>Digitizing molecular structure information of diverse compounds is essential for AI-driven drug discovery. While a significant amount of molecular structure data is already stored and managed in digital formats for in silico predictions of drug stability and side effects, a significant portion remains in analog documents. To address this, deep learning-based optical chemical structure recognition (OCSR) technologies have been developed. Although these models excel with abundant digitally rendered chemical structure images, they underperform on hand-drawn images due to lack of sufficient training data for such images. This study proposes OCSAug, a diffusion model-based data augmentation technique to improve OCSR performance on hand-drawn molecular images. Using a denoising diffusion probabilistic model to capture irregularities and distortions, and the RePaint algorithm to generate augmented data, OCSAug significantly enhances recognition accuracy. Evaluation on the DECIMER dataset shows an improvement of 1.918–3.820 times over results without augmentation.</p>

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OCSAug: diffusion-based optical chemical structure data augmentation for improved hand-drawn chemical structure image recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Jin Hyuk Kim,
  • Jonghwan Choi

摘要

Digitizing molecular structure information of diverse compounds is essential for AI-driven drug discovery. While a significant amount of molecular structure data is already stored and managed in digital formats for in silico predictions of drug stability and side effects, a significant portion remains in analog documents. To address this, deep learning-based optical chemical structure recognition (OCSR) technologies have been developed. Although these models excel with abundant digitally rendered chemical structure images, they underperform on hand-drawn images due to lack of sufficient training data for such images. This study proposes OCSAug, a diffusion model-based data augmentation technique to improve OCSR performance on hand-drawn molecular images. Using a denoising diffusion probabilistic model to capture irregularities and distortions, and the RePaint algorithm to generate augmented data, OCSAug significantly enhances recognition accuracy. Evaluation on the DECIMER dataset shows an improvement of 1.918–3.820 times over results without augmentation.