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. Constructed from 13 public repositories, the SCOPE dataset 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, significantly outperforming state-of-the-art methods across various DTI prediction tasks. Additionally, SCOPE-DTI provides a user-friendly interface and database. We further validate its effectiveness by experimentally identifying anticancer targets of Ginsenoside Rh1. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.

本研究针对深度学习药物靶点相互作用预测方法在数据多样性和建模复杂性上的局限性，提出了SCOPE-DTI框架。该框架结合了扩大100倍的半诱导人类DTI数据集与先进的深度学习模型，显著提升了多种DTI预测任务的性能，并提供了用户友好的界面及数据库，加速药物发现研究。

SCOPE-DTI: Semi-Inductive Dataset Construction and Framework
  Optimization for Practical Usability Enhancement in Deep Learning-Based Drug
  Target Interaction Prediction

半诱导数据集构建与框架优化以提升深度学习基础的药物靶点相互作用预测的实用性