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

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.

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Publication Details

Journal
Nature Communications
Published
2025-12-13
DOI
https://doi.org/10.1038/s41467-025-66311-9
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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article

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

Chang Su, Yi‐Ling Lai, Shih‐Chung Yen, Xiang Ji et al.
1 citations
Nature Communications
Computational Drug Discovery Methods
1.50
article

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

Chang Su, Yi‐Ling Lai, Shih‐Chung Yen, Xiang Ji, Yang-Chi-Dung Lin, Ke Chen, Hsien‐Da Huang, Yixian Huang, Yangyi Zhang, Kai Wei, Jiayi Fu, Xiukun Lin, Shidong Cui, Yuming Zhou, Arieh Warshel, Ziyue Zhang, Yi-Gang Chen, Tao Zhang, Zihao Zhu
article en
1 citations

Abstract

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.

Nature CommunicationsVol. 16(1)
University of Southern California (US), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN), Chinese University of Hong Kong, Shenzhen (CN)
Chinese University of Hong Kong
Openalex Percentile: Top 11%
Computational Drug Discovery Methods
1.50
DOIOpen Access PDFOpenAlex
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