The discovery of drug-target interactions (DTIs) is a pivotal process in pharmaceutical development. Computational approaches are a promising and efficient alternative to tedious and costly wet-lab experiments for predicting novel DTIs from numerous candidates. Recently, with the availability of abundant heterogeneous biological information from diverse data sources, computational methods have been able to leverage multiple drug and target similarities to boost the performance of DTI prediction. Similarity integration is an effective and flexible strategy to extract crucial information across complementary similarity views, providing a compressed input for any similarity-based DTI prediction model. However, existing similarity integration methods filter and fuse similarities from a global perspective, neglecting the utility of similarity views for each drug and target. In this study, we propose a Fine-Grained Selective similarity integration approach, called FGS, which employs a local interaction consistency-based weight matrix to capture and exploit the importance of similarities at a finer granularity in both similarity selection and combination steps. We evaluate FGS on five DTI prediction datasets under various prediction settings. Experimental results show that our method not only outperforms similarity integration competitors with comparable computational costs, but also achieves better prediction performance than state-of-the-art DTI prediction approaches by collaborating with conventional base models. Furthermore, case studies on the analysis of similarity weights and on the verification of novel predictions confirm the practical ability of FGS.

翻译：药物-靶标相互作用（DTI）的发现是药物开发中的关键过程。计算方法是从众多候选物中预测新型DTI的一种有前景且高效的替代方案，能够避免繁琐且昂贵的湿实验。近年来，随着来自不同数据源的丰富异质生物学信息的可用性，计算方法能够利用多种药物和靶标相似性来提升DTI预测性能。相似度整合是一种有效且灵活的策略，可从互补的相似度视角中提取关键信息，为任何基于相似度的DTI预测模型提供压缩输入。然而，现有相似度整合方法从全局视角过滤和融合相似性，忽略了相似度视角对每种药物和靶标的效用。在本研究中，我们提出了一种名为FGS的细粒度选择性相似度整合方法，该方法采用基于局部相互作用一致性的权重矩阵，在相似度选择和组合步骤中以更细粒度捕获并利用相似性的重要性。我们在五种DTI预测数据集上、多种预测设置下评估了FGS。实验结果表明，我们的方法不仅在计算成本相当的情况下优于相似度整合竞争方法，而且通过与常规基础模型协作，取得了比最先进DTI预测方法更优的预测性能。此外，关于相似度权重分析及新预测验证的案例研究证实了FGS的实际应用能力。