构建大规模平衡数据集，提升深度学习药物靶点预测实用性能
SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction
- 融合13个公开数据源构建超大规模半归纳数据集，规模达基准数据100倍
- 结合3D分子与蛋白表示、图神经网络和双线性注意力机制，显著超越现有方法
- 提供易用接口与数据库，适用于抗癌药物靶点发现等实际研究场景
基于深度学习的药物-靶点相互作用（DTI）预测方法表现优异，但真实应用受限于数据多样性不足与建模复杂性。为此，我们提出SCOPE-DTI框架，整合大规模、平衡的半归纳人类DTI数据集与先进深度学习模型。该数据集源自13个公共资源，数据量相比常见基准（如Human数据集）提升高达100倍。SCOPE模型融合三维蛋白质与化合物表征、图神经网络及双线性注意力机制，有效捕捉跨域相互作用模式，在多种DTI预测任务中显著优于现有最优方法。此外，SCOPE-DTI提供友好的用户界面与数据库。我们通过实验验证其在识别人参皂苷Rh1抗癌靶点中的有效性。通过提供全面数据、先进建模与可访问工具，SCOPE-DTI加速药物发现研究。
原文摘要 · Abstract (English)
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.
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