SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910871835377664 |
|---|---|
| author | Chen, Yigang Ji, Xiang Zhang, Ziyue Zhou, Yuming Lin, Yang-Chi-Dung Huang, Hsi-Yuan Zhang, Tao Lai, Yi Chen, Ke Su, Chang Lin, Xingqiao Zhu, Zihao Zhang, Yanggyi Wei, Kangping Fu, Jiehui Huang, Yixian Cui, Shidong Yen, Shih-Chung Warshel, Ariel Huang, Hsien-Da |
| author_facet | Chen, Yigang Ji, Xiang Zhang, Ziyue Zhou, Yuming Lin, Yang-Chi-Dung Huang, Hsi-Yuan Zhang, Tao Lai, Yi Chen, Ke Su, Chang Lin, Xingqiao Zhu, Zihao Zhang, Yanggyi Wei, Kangping Fu, Jiehui Huang, Yixian Cui, Shidong Yen, Shih-Chung Warshel, Ariel Huang, Hsien-Da |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction Chen, Yigang Ji, Xiang Zhang, Ziyue Zhou, Yuming Lin, Yang-Chi-Dung Huang, Hsi-Yuan Zhang, Tao Lai, Yi Chen, Ke Su, Chang Lin, Xingqiao Zhu, Zihao Zhang, Yanggyi Wei, Kangping Fu, Jiehui Huang, Yixian Cui, Shidong Yen, Shih-Chung Warshel, Ariel Huang, Hsien-Da Machine Learning Artificial Intelligence Quantitative Methods 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. |
| title | SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2503.09251 |