Author:
Zhai Haixia,Hou Hongli,Luo Junwei,Liu Xiaoyan,Wu Zhengjiang,Wang Junfeng
Abstract
Abstract
Background
Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.
Results
Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA.
Conclusions
The experimental results show that DGDTA can predict DTA more accurately than some other methods.
Funder
Innovative and Scientific Research Team of Henan Polytechnic University
Innovation Project of New Generation Information Technology
National Natural Science Foundation of China
Young Elite Teachers in Henan Province
Doctor Foundation of Henan Polytechnic University
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology