Biotechnology and Bioprocess Engineering 2020; 25(6): 895-930  
Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches
Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, and Hojung Nam
Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam*
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
Tel: +82-62-715-2641
E-mail: hjnam@gist.ac.kr
Received: February 13, 2020; Revised: May 27, 2020; Accepted: June 3, 2020; Published online: December 31, 2020.
© The Korean Society for Biotechnology and Bioengineering. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
Keywords: drug discovery, artificial intelligence, datadriven, machine learning


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