MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Journal: Biomolecules
PMID:

Abstract

In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound-protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.

Authors

  • Shuang Wang
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. S1507038@st.nuc.edu.cn.
  • Mingjian Jiang
    College of Information Science and Engineering, Ocean University of China, Qingdao, China. jmj@stu.ouc.edu.cn.
  • Shugang Zhang
    College of Information Science and Engineering, Ocean University of China, Qingdao, China. zhangshugang@hotmail.com.
  • XiaoFeng Wang
    Indiana University Bloomington.
  • Qing Yuan
    College of Information Science and Engineering, Ocean University of China, Qingdao, China. yuanqing@stu.ouc.edu.cn.
  • Zhiqiang Wei
  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.