ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (9): 2051-2065.doi: 10.7544/issn1000-1239.20210134

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Protein-Drug Interaction Prediction Based on Attention Feature Fusion

Hua Yang1, Li Jinxing1, Feng Zhenhua2, Song Xiaoning1, Sun Jun1, Yu Dongjun3   

  1. 1(School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214100);2(Department of Computer Science, University of Surrey, Guildford, UK GU2 7XH);3(School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094)
  • Online:2022-09-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program(2017YFC1601800), the National Natural Science Foundation of China(61876072, 61902153,62072243,61772273), the China Postdoctoral Science Foundation(2018T110441), and the Six Talent Peaks Project of Jiangsu Province (XYDXX-012).

Abstract: Drugs usually work by inhibiting or activating the active reactions of certain proteins in the human body, so the prediction of the interactions between proteins and drugs is very important for the screening of new drugs. However, it takes a lot of manpower and material resources to carry out this kind of wet experiment using traditional methods. To resolve this problem, we propose a protein-drug interaction prediction algorithm based on the self-attention mechanism and multi-drug feature fusion. Firstly, the Morgan fingerprint based on drug molecular structure characteristics, the Mol2Vec representation vector, and the features extracted by the messaging network are reasonably fused. Secondly, the fusion results are used to weigh the protein features extracted by dense convolution. After that, the self-attentional mechanism and bidirectional gating circulatory unit are used to predict protein-drug interactions by combining their characteristics. Finally, an applicable prediction system based on the training model is designed, which demonstrates the specific use cases and effects of the proposed method in drug screening for the Alzheimer disease. The experimental results show that the proposed algorithm achieves better prediction performance on BindingDB, Kinase, Human and C.elegans datasets compared with the existing prediction methods. The AUC values achieve 0.963, 0.937, 0.983, 0.990 on the four datasets, demonstrating significant superiority over the other algorithms.

Key words: protein-drug interaction, drug screening, feature fusion, DenseNet, self-attention mechanism

CLC Number: