SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions

Xiaokun Li, Qiang Yang, Gongning Luo, Long Xu, Weihe Dong, Wei Wang, Suyu Dong, Kuanquan Wang, Ping Xuan, Xin Gao

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results: In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI interaction. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on SARS-CoV-2 shows that our model is a powerful tool for identifying DTI interactions in real life.
Original languageEnglish (US)
JournalBioinformatics Advances
DOIs
StatePublished - Aug 26 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-08-31
Acknowledged KAUST grant number(s): FCC/1/1976-44-01, FCC/1/1976-45-01, REI/1/5234-01-01
Acknowledgements: The project is supported by the National Natural Science Foundation of China (Nos. 81273649, 61501132, 61672181, 62001144, 62202092, 62272135), the Natural Science Foundation of Heilongjiang Province (Nos. LH2019F049, LH2019A029), the China Postdoctoral Science Foundation (No. 2019M650069), the Research Funds for the Central Universities (No. 3072019CFT0603) and the Fund for Young Innovation Team of Basic Scientific Research in Heilongjiang Province (No. RCYJTD201805), Fund from China Scholarship Council (CSC), and the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No FCC/1/1976-44-01, FCC/1/1976-45-01, and REI/1/5234-01-01. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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