Li Chang, Mi Jia, Wang Han, Liu Zhikang, Gao Jingyang, Wan Jing
The College of Information Science and Technology, Beijing University of Chemical Technology, North Third Ring Road 15, Beijing, 100029, China.
The College of Information Science and Technology, Beijing University of Chemical Technology, North Third Ring Road 15, Beijing, 100029, China.
Comput Biol Chem. 2025 Oct;118:108449. doi: 10.1016/j.compbiolchem.2025.108449. Epub 2025 Apr 10.
Accurately predicting drug-target interactions (DTI) is crucial for drug discovery and can reduce drug development costs. Recent deep learning-based DTI predictions have demonstrated promising performance, but they still face two challenges: (i) The over-reliance on the extraction of local features and insufficient learning of global features limit the model's performance. (ii) The lack of effective fusion of drug-target interaction features leads to the lack of interpretability of the model. To address these challenges, we propose a new model for predicting drug-target interactions based on multi-order gated convolution and multi-attention fusion, MGMA-DTI. The drug feature encoder obtains a two-dimensional molecular graph based on the drug's SMILES string and uses a graph convolutional neural network to encode the drug features. The protein encoder is based on a multi-order gated convolution, which enhances the model's ability to capture global feature between amino acid sequences. In order to better achieve interactive learning between drugs and proteins, we designed a multi-attention fusion module that effectively captures the drug-target interaction features. Experimental results show that MGMA-DTI outperforms other baseline models on three benchmark datasets: BindingDB, BioSNAP, and Human. Case studies further demonstrate that the model provides valuable insights for drug discovery. In addition, our model provides molecular-level interpretability, which can provide more scientifically meaningful guidance.
准确预测药物-靶点相互作用（DTI）对于药物发现至关重要，并且可以降低药物开发成本。最近基于深度学习的DTI预测已展现出良好的性能，但它们仍面临两个挑战：（i）对局部特征提取的过度依赖以及对全局特征学习的不足限制了模型的性能。（ii）缺乏对药物-靶点相互作用特征的有效融合导致模型缺乏可解释性。为应对这些挑战，我们提出了一种基于多阶门控卷积和多注意力融合的预测药物-靶点相互作用的新模型，即MGMA-DTI。药物特征编码器基于药物的SMILES字符串获得二维分子图，并使用图卷积神经网络对药物特征进行编码。蛋白质编码器基于多阶门控卷积，增强了模型捕捉氨基酸序列之间全局特征的能力。为了更好地实现药物与蛋白质之间的交互学习，我们设计了一个多注意力融合模块，该模块有效地捕捉药物-靶点相互作用特征。实验结果表明，MGMA-DTI在三个基准数据集BindingDB、BioSNAP和Human上优于其他基线模型。案例研究进一步证明，该模型为药物发现提供了有价值的见解。此外，我们的模型提供了分子水平的可解释性，能够提供更具科学意义的指导。
