Xu Jiahao, Ci Lei, Zhu Bo, Zhang Guanhua, Jiang Linhua, Ye-Lehmann Shixin, Long Wei
School of Information Engineering, Huzhou University, Huzhou 313000, China.
Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China.
J Chem Inf Model. 2025 Jan 27;65(2):981-996. doi: 10.1021/acs.jcim.4c01828. Epub 2025 Jan 7.
Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets─Davis, KIBA, and Metz─and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.
药物-靶点亲和力（DTA）预测是药物发现与开发的基石，它为深入了解候选药物与其生物靶点之间的复杂相互作用提供了关键见解。尽管其至关重要，但现有方法在从分子图中捕捉全面的全局特征时往往面临重大局限，而这些特征对于准确表征药物特性至关重要。此外，蛋白质特征提取主要局限于一维氨基酸序列，无法充分代表蛋白质的空间结构和复杂功能区域。这些缺点阻碍了能够全面阐明药物-靶点相互作用潜在机制的模型的开发。为了克服这些挑战，我们提出了一种基于序列和图模态的多模态、多尺度药物-靶点亲和力（MMSG-DTA）预测模型。该模型将图神经网络与Transformer相结合，以有效捕捉分子图的局部节点级特征和全局结构特征。此外，采用基于图的模态来改进从氨基酸序列中提取蛋白质特征的方法。为了进一步提高模型的性能，引入了基于注意力的特征融合模块来整合不同类型的特征，从而增强其表示能力和鲁棒性。我们在三个公共基准数据集（Davis、KIBA和Metz）上对MMSG-DTA进行了评估，实验结果表明，所提出的模型在DTA预测方面优于几种现有最先进的方法。这些发现凸显了MMSG-DTA在提高药物-靶点相互作用建模的准确性和鲁棒性方面的有效性。
