HMBVIP: a novel hierarchical multi-bio-view intelligent prediction networks for drug–target interaction prediction
Published in Journal of Chemical Information and Modeling, 2025
Hailong Yang, Qiao Ning, Ze Song, Yue Chen, Guanjin Wang, Zhaohong Deng, Yun Zuo, Yuxi Ge, Shudong Hu. JCIM 2025 (SCI Area-2). https://doi.org/10.1021/acs.jcim.5c01142
HMBVIP
Drug−target interaction (DTI) prediction is crucial in drug discovery. Recent advances in multiview learning have made it possible to automatically extract complex features from multiple perspectives. Multiview models, which integrate diverse biological data sources, have demonstrated improved prediction accuracy and robustness. However, current approaches still face major limitations: (1) reliance on single-scale sequence tokenizers that fail to capture biological information across different granularities and (2) shallow, single-layer integration of data views that overlook the hierarchical nature of biological systems. To tackle these challenges, we propose the concept of “bio-token” and design a multiscale biological tokenizer that captures biological features at varying resolutions. We also introduce a novel hierarchical multi-bio-view learning (HMBV) approach, implemented in an end-toend DTI prediction network termed HMBVIP. The hierarchical multiview fusion enriches hidden representations with multidimensional biological context, thereby enhancing both prediction accuracy and biologically meaningful interpretability. The results on benchmark data sets demonstrate that HMBVIP consistently outperforms current state-of-the-art models.
