R-DTI: Drug Target Interaction Prediction Based on Second-Order Relevance Exploration

作者：Yang Hua, Tianyang Xu, Xiaoning Song, Zhenhua Feng, Rui Wang, Wenjie Zhang, Xiaojun Wu · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2025 · DOI：10.1609/aaai.v39i16.33909 · 被引用次数：3 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science

Drug Target Interaction (DTI) prediction has witnessed promising performance boosts accompanied by advanced multimodal feature extraction. However, existing approaches suffer from two main difficulties. First, the complex protein structures cannot be well represented by current protein-sequence-based feature extractors. Second, the gap between protein and drug features increases the vulnerability of the obtained classifier thus degrading the prediction robustness. To address these issues, we propose a novel R-DTI method by exploring the second-order relevance in both protein structural feature extraction and DTI prediction phases. Specifically, we construct a pre-trained structural feature extractor that mines the atomic relevance of each amino acid. Then, an inter-feature structure-preserved Riemannian network is designed to expand the existing protein extraction patterns. To improve the prediction robustness, we also develop a Riemannian classifier that uses the second-order protein-drug relevance with a unified feature space. Extensive experimental results demonstrate the merits and superiority of our R-DTI against the state-of-the-art, achieving 1.4% and 1.9% higher AUC-ROC on the BindingDB and DrugBank datasets, respectively.