Accurate prediction of drug-target interactions (DTI) is pivotal for drug discovery, yet existing methods often fail to address challenges like cross-domain generalization, cold-start prediction, and interpretability. In this work, we propose CDI-DTI, a novel cross-domain interpretable framework for DTI prediction, designed to overcome these limitations. By integrating multi-modal features-textual, structural, and functional-through a multi-strategy fusion approach, CDI-DTI ensures robust performance across different domains and in cold-start scenarios. A multi-source cross-attention mechanism is introduced to align and fuse features early, while a bidirectional cross-attention layer captures fine-grained intra-modal drug-target interactions. To enhance model interpretability, we incorporate Gram Loss for feature alignment and a deep orthogonal fusion module to eliminate redundancy. Experimental results on several benchmark datasets demonstrate that CDI-DTI significantly outperforms existing methods, particularly in cross-domain and cold-start tasks, while maintaining high interpretability for practical applications in drug-target interaction prediction.

翻译：准确预测药物-靶点相互作用（DTI）对于药物发现至关重要，然而现有方法往往难以应对跨域泛化、冷启动预测和可解释性等挑战。本文提出CDI-DTI，一种新颖的跨域可解释DTI预测框架，旨在克服这些局限。通过多策略融合方法整合文本、结构和功能等多模态特征，CDI-DTI确保了在不同领域及冷启动场景下的鲁棒性能。我们引入了多源交叉注意力机制以实现特征的早期对齐与融合，同时采用双向交叉注意力层捕获细粒度的模态内药物-靶点相互作用。为增强模型可解释性，我们整合了用于特征对齐的Gram损失函数以及消除冗余的深度正交融合模块。在多个基准数据集上的实验结果表明，CDI-DTI显著优于现有方法，尤其在跨域和冷启动任务中表现突出，同时为药物-靶点相互作用预测的实际应用保持了高度的可解释性。