DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.

Journal: Combinatorial chemistry & high throughput screening
Published Date:

Abstract

INTRODUCTION: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string.

Authors

  • Shudong Wang
    College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong,China.
  • Jiali Liu
    Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Mao Ding
    Department of Neurology Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033,China | College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, China.
  • Yijun Gao
    Department of Physiology, Shandong Provincial Key Laboratory of Pathogenesis and Prevention of Neurological Disorders and State Key Disciplines: Physiology, School of Basic Medicine, Qingdao University, Qingdao,China.
  • Dayan Liu
    College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Qingyu Tian
    College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong,China.
  • Jinfu Zhu
    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211016, China.