Accurate prediction of drug-drug interactions (DDIs) is crucial for ensuring drug safety and supporting pharmaceutical development. Existing methods still face challenges in jointly modeling molecular structural information and biomedical semantic contexts. To address this issue, we propose DSAT-DDI, a Dual-channel Str...
Yu Li, Jia-Ming Liu, Yue-Chao Li et al.· IEEE journal of biomedical a...· 0 citations
DC-MetaMG, a deep learning framework based on a causal disentanglement strategy that models association responses as the synergistic interplay between two complementary mechanisms: static binding and dynamic regulation, is proposed, demonstrating its suitability for training scenarios involving complex biological infor...
Runzhou Tang, Xi Zhou, Yujie Qi et al.· IEEE journal of biomedical a...· 0 citations
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking result...
Bowei Zhao, Hui Zhao, Yu-An Huang et al.· IEEE transactions on computa...· 0 citations
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