He Chengxin, Zhao Zhenjiang, Wang Xinye, Zheng Huiru, Duan Lei, Zuo Jie
School of Computer Science, Sichuan University, Chengdu 610065, China; College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
School of Computer Science, Sichuan University, Chengdu 610065, China.
Methods. 2025 Feb;234:10-20. doi: 10.1016/j.ymeth.2024.11.010. Epub 2024 Nov 15.
Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.
预测药物-靶点相互作用（DTI）对于药物研发至关重要。随着生物和化学技术的快速发展，用于DTI预测的计算方法正成为一种有前景的途径。然而，在DTI预测场景中，针对冷启动问题的解决方案很少，因为这些方法依赖于现有的相互作用信息来支持其建模。因此，在现有工作中，对于相互作用数据有限的新药或靶点，它们无法有效地预测DTI。为此，我们提出了一种基于元学习的图变换器方法，名为MGDTI（基于元学习的用于药物-靶点相互作用预测的图变换器的缩写）来填补这一空白。从技术上讲，我们利用药物-药物相似性和靶点-靶点相似性作为额外信息来缓解相互作用的稀缺性。此外，我们通过元学习训练MGDTI以适应冷启动任务。而且，我们采用图变换器通过捕获长程依赖来防止过度平滑。在基准数据集上的大量结果表明，MGDTI在冷启动场景下的DTI预测中是有效的。
