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<p>Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. SCOPE-DTI is constructed from 13 public repositories and expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. The SCOPE model integrates three-dimensional protein and compound representations, graph neural networks, and bilinear attention mechanisms to effectively capture cross domain interaction patterns and outperform state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further demonstrate its effectiveness by experimentally identifying anticancer targets of two bioactive natural compounds. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.</p>

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Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月13日

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  • Yigang Chen,
  • Xiang Ji,
  • Ziyue Zhang,
  • Zihao Zhu,
  • Yuming Zhou,
  • Chang Su,
  • Yang-Chi-Dung Lin,
  • Hsi-Yuan Huang,
  • Kangping Wei,
  • Yi Lai,
  • Ke Chen,
  • Xingqiao Lin,
  • Yangyi Zhang,
  • Jiehui Fu,
  • Yixian Huang,
  • Shidong Cui,
  • Shih-Chung Yen,
  • Tao Zhang,
  • Arieh Warshel,
  • Hsien-Da Huang

摘要

Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. SCOPE-DTI is constructed from 13 public repositories and expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. The SCOPE model integrates three-dimensional protein and compound representations, graph neural networks, and bilinear attention mechanisms to effectively capture cross domain interaction patterns and outperform state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further demonstrate its effectiveness by experimentally identifying anticancer targets of two bioactive natural compounds. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.