SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction
Yi-Gang Chen, Xiang Ji, Ziyue Zhang, Yuming Zhou, Yang-Chi-Dung Lin, Hsi‐Yuan Huang, Tao Zhang, Lai, Yi, Ke Chen, Chang Su, Lin, Xingqiao, Zihao Zhu, Zhang, Yanggyi, Wei, Kangping, Jiayi Fu, Huang, Yixian, Shidong Cui, Shih‐Chung Yen, Warshel, Ariel, Hsien‐Da Huang · arXiv (Cornell University) · 2025
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.