Survey of Machine Learning Techniques in Drug Discovery.

Journal: Current drug metabolism
Published Date: Jan 1, 2019

Abstract

BACKGROUND: Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery.

Authors

  • Natalie Stephenson
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Emily Shane
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Jessica Chase
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Jason Rowland
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • David Ries
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Nicola Justice
    Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Leong Chan
    School of Business, Pacific Lutheran University, Tacoma, WA 98447, USA. chanla@plu.edu.
  • Renzhi Cao
    Department of Computer Science, Pacific Lutheran University, Tacoma, WA, 98447, USA.

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