Drug-target Interaction Prediction By Combining Transformer and Graph Neural Networks


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Abstract

Background:The prediction of drug-target interactions (DTIs) plays an essential role in drug discovery. Recently, deep learning methods have been widely applied in DTI prediction. However, most of the existing research does not fully utilize the molecular structures of drug compounds and the sequence structures of proteins, which makes these models unable to obtain precise and effective feature representations.

Methods:In this study, we propose a novel deep learning framework combining transformer and graph neural networks for predicting DTIs. Our model utilizes graph convolutional neural networks to capture the global and local structure information of drugs, and convolutional neural networks are employed to capture the sequence feature of targets. In addition, the obtained drug and protein representations are input to multi-layer transformer encoders, respectively, to integrate their features and generate final representations.

Results:The experiments on benchmark datasets demonstrated that our model outperforms previous graph-based and transformer-based methods, with 1.5% and 1.8% improvement in precision and 0.2% and 1.0% improvement in recall, respectively. The results indicate that the transformer encoders effectively extract feature information of both drug compounds and proteins.

Conclusion:Overall, our proposed method validates the applicability of combining graph neural networks and transformer architecture in drug discovery, and due to the attention mechanisms, it can extract deep structure feature data of drugs and proteins.

About the authors

Junkai Liu

School of Electronic and Information Engineering, Suzhou University of Science and Technology

Email: info@benthamscience.net

Yaoyao Lu

Suzhou University of Science and Technology, School of Electronic and Information Engineering

Email: info@benthamscience.net

Shixuan Guan

School of Electronic and Information Engineering, Suzhou University of Science and Technology

Email: info@benthamscience.net

Tengsheng Jiang

Gusu School, Nanjing Medical University

Email: info@benthamscience.net

Yijie Ding

Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of Chin

Email: info@benthamscience.net

Qiming Fu

School of Electronic and Information Engineering, Suzhou University of Science and Technology

Email: info@benthamscience.net

Zhiming Cui

School of Electronic and Information Engineering, Suzhou University of Science and Technology

Email: info@benthamscience.net

Hongjie Wu

School of Electronic and Information Engineering, Suzhou University of Science and Technology

Author for correspondence.
Email: info@benthamscience.net

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