School of Electronic and Information Engineering, Suzhou University of Science and Technology, Su Zhou 215009, P. R. China.
Gusu School, Nanjing Medical University, Su Zhou 215009, P. R. China.
J Bioinform Comput Biol. 2024 Oct;22(5):2450024. doi: 10.1142/S0219720024500240. Epub 2024 Nov 11.
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end, we propose a new deep learning framework that combines the structural information and sequence features of proteins to provide comprehensive feature representation through bimodal fusion. This framework not only integrates the topological adaptive graph convolutional network and multi-head attention mechanism, but also introduces a self-masked attention mechanism to ensure that each protein binding site can focus on its own unique features and its interaction with the ligand. Experimental results on multiple public datasets show that our method significantly outperforms traditional machine learning and graph neural network methods in predictive performance. In addition, our method can effectively identify and explain key molecular interactions, providing new insights into understanding the complex relationship between drugs and targets.
在药物发现过程中，准确预测药物-靶标相互作用对于加速新药的开发至关重要。然而，现有的方法在处理复杂的生物分子相互作用时仍然面临许多挑战。为此，我们提出了一种新的深度学习框架，该框架结合了蛋白质的结构信息和序列特征，通过双模态融合提供全面的特征表示。该框架不仅集成了拓扑自适应图卷积网络和多头注意力机制，还引入了自掩蔽注意力机制，以确保每个蛋白质结合位点能够专注于其自身独特的特征及其与配体的相互作用。在多个公共数据集上的实验结果表明，我们的方法在预测性能上明显优于传统的机器学习和图神经网络方法。此外，我们的方法可以有效地识别和解释关键的分子相互作用，为理解药物和靶标之间的复杂关系提供新的见解。
