Wang Jihong, He Ruijia, Wang Xiaodan, Li Hongjian, Lu Yulei
School of Computer, Guangdong University of Education, Guangzhou 510310, China.
School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan 528458, China.
Molecules. 2025 Jan 12;30(2):274. doi: 10.3390/molecules30020274.
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. For the target side, it combines MSCNN with Transformer modules to capture both local and global features effectively. The extracted features are then weighted and fused, enabling comprehensive feature representation to enhance the predictive power of the model. Experimental results on the Davis dataset demonstrate that MCF-DTI achieves an AUC of 0.9746 and an AUPR of 0.9542, outperforming other state-of-the-art models. Our case study demonstrates that our model effectively validated several known drug-target relationships in lung cancer and predicted the therapeutic potential of certain preclinical compounds in treating lung cancer. These findings contribute valuable insights for subsequent drug repurposing efforts and novel drug development.
预测药物-靶点相互作用（DTIs）是新药开发和药物重新利用过程中的关键一步。在本文中，我们提出了一种名为MCF-DTI的新型药物-靶点预测模型。该模型利用药物的SMILES表示和靶点的序列特征，采用具有并行共享权重模块的多尺度卷积神经网络（MSCNN）从药物端提取特征。对于靶点端，它将MSCNN与Transformer模块相结合，以有效捕捉局部和全局特征。然后对提取的特征进行加权和融合，实现全面的特征表示，从而增强模型的预测能力。在Davis数据集上的实验结果表明，MCF-DTI的AUC为0.9746，AUPR为0.9542，优于其他现有最先进的模型。我们的案例研究表明，我们的模型有效地验证了肺癌中几种已知的药物-靶点关系，并预测了某些临床前化合物在治疗肺癌方面的治疗潜力。这些发现为后续的药物重新利用工作和新药开发提供了有价值的见解。
