计算机科学
人工智能
卷积神经网络
变压器
局部场电位
深度学习
解耦(概率)
模式识别(心理学)
机器学习
工程类
电压
生物
电气工程
控制工程
神经科学
作者
Tianyu Wang,Wenming Yang,Jie Chen,Yonghong Tian,Dong‐Qing Wei
标识
DOI:10.1109/bibm55620.2022.9995344
摘要
Drug-target interaction(DTI) prediction is one of the most important topics in drug design and drug development, and deep learning approaches have achieved state-of-the-art performance in this field. However, the current methods are difficult to successfully combine the local and global features of drug molecules and protein sequences, while ignoring the modeling of complicated interaction mechanisms, which leads to a certain limitation of prediction performance. To overcome this barrier, we propose an end-to-end method based on Convolutional Neural Network (CNN) and Transformer to predict DTI problems, named ConformerDTI. The CNN and Transformer branches extract features from the simplified molecular input line entry system (SMILES) string of drugs and the amino acid sequence of proteins, respectively. The local and global features are coupled by the mutual transfer of the two branches through cross attention. Decoupling of local and global features in parallel leverages CNN’s power in extracting local features as well as the efficiency of Transformer at global processing. I n addition, ConformerDTI exploits the convolutional interaction network to model the interaction mechanism, both drugs and targets are convoluted by dynamic filters generated based on each other. Experimental results demonstrate that our model has better prediction performance than the most advanced deep learning methods on three different datasets. Furthermore, this performance improvement was validated by ablation experiments.
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