Transformer and Graph Transformer-Based Prediction of Drug-Target Interactions


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Abstract

Background:As we all know, finding new pharmaceuticals requires a lot of time and money, which has compelled people to think about adopting more effective approaches to locate drugs. Researchers have made significant progress recently when it comes to using Deep Learning (DL) to create DTI

Methods:Therefore, we propose a deep learning model that applies Transformer to DTI prediction. The model uses a Transformer and Graph Transformer to extract the feature information of protein and compound molecules, respectively, and combines their respective representations to predict interactions.

Results:We used Human and C.elegans, the two benchmark datasets, evaluated the proposed method in different experimental settings and compared it with the latest DL model.

Conclusion:The results show that the proposed model based on DL is an effective method for the classification and recognition of DTI prediction, and its performance on the two data sets is significantly better than other DL based methods.

About the authors

Meiling Qian

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

Author for correspondence.
Email: info@benthamscience.net

Weizhong Lu

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

Author for correspondence.
Email: info@benthamscience.net

Yu Zhang

, Suzhou Industrial Park Institute of Services Outsourcing

Email: info@benthamscience.net

Junkai Liu

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

Email: info@benthamscience.net

Yaoyao Lu

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

Email: info@benthamscience.net

Haiou Li

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

Email: info@benthamscience.net

Qiming Fu

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

Email: info@benthamscience.net

Jiyun Shen

Provincial Key Laboratory for Computer Information Processing Technology, Soochow University

Email: info@benthamscience.net

Yongbiao Xiao

School of Artificial Intelligence and Computer Science, JiangNan University

Email: info@benthamscience.net

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