Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features.

Authors

  • Maya Hirohara
    Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.
  • Yutaka Saito
    National Cancer Center Hospital, Tokyo, Japan (Y.S.).
  • Yuki Koda
    Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.
  • Kengo Sato
    Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
  • Yasubumi Sakakibara
    Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan yasu@bio.keio.ac.jp.