The recent progress of deep-learning-based in silico prediction of drug combination.

Journal: Drug discovery today
Published Date:

Abstract

Drug combination therapy has become a common strategy for the treatment of complex diseases. There is an urgent need for computational methods to efficiently identify appropriate drug combinations owing to the high cost of experimental screening. In recent years, deep learning has been widely used in the field of drug discovery. Here, we provide a comprehensive review on deep-learning-based drug combination prediction algorithms from multiple aspects. Current studies highlight the flexibility of this technology in integrating multimodal data and the ability to achieve state-of-art performance; it is expected that deep-learning-based prediction of drug combinations should play an important part in future drug discovery.

Authors

  • Haoyang Liu
  • Zhiguang Fan
    Department of Drug and Vaccine Research, Guangzhou Laboratory, Guangzhou 513000, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
  • Jie Lin
    Department of Reproductive Medicine, Zigong Hospital of Women and Children Health Care, Zigong, China.
  • Yuedong Yang
    Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.
  • Ting Ran
    Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, China.
  • Hongming Chen
    Hit Discovery, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, 431 83, Mölndal, Sweden.