Background: The computational identification of drug-target interaction (DTI) is pivotal in drug discovery and chemical genomics. Current network-based approaches model DTI as a link prediction problem utilizing bipartite graphs. However, simplistic representations fail to encapsulate crucial biological semantic information, and how to effectively integrate molecular structure features with graph neural networks has emerged as a non-negligible challenge in the DTI domain. Results: To address these issues, we propose a Dual-Channel Neural Network for Drug-Target Interaction (DCDTI) prediction model based on GLTransformer, aiming to learn high-quality representations of the topological structures of drugs and targets with precision. Extensive experiments validate that DCDTI surpasses state-of-the-art methods. Case studies have further confirmed its generalization ability in actual DTI scenarios. Conclusion: DCDTI provides a powerful method for DTI prediction, which can also serve as a screening tool for studies of drug discovery.
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Share and Cite
Zhang, P., Zeng, Y. (2026) DCDTI: Dual-Channel Neural Network for Drug-Target Interaction Prediction. Scientific Research Bulletin, 3(1), 95-106.