Survey of Machine Learning Techniques in Drug Discovery
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Published:2019-05-22
Issue:3
Volume:20
Page:185-193
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ISSN:1389-2002
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Container-title:Current Drug Metabolism
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language:en
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Short-container-title:CDM
Author:
Stephenson Natalie1, Shane Emily1, Chase Jessica1, Rowland Jason1, Ries David1, Justice Nicola2, Zhang Jie3, Chan Leong4, Cao Renzhi1
Affiliation:
1. Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, United States 2. Department of Mathematics, Pacific Lutheran University, Tacoma, WA 98447, United States 3. Key Laboratory of Hebei Province for Plant Physiology and Molecular Pathology, College of Life Sciences, Hebei Agricultural University, Baoding, China 4. School of Business, Pacific Lutheran University, Tacoma, WA 98447, United States
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.Methods:We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery.Results:Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year.Conclusion:The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.
Publisher
Bentham Science Publishers Ltd.
Subject
Clinical Biochemistry,Pharmacology
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