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KMID : 1094720200250060895
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Biotechnology and Bioprocess Engineering
2020 Volume.25 No. 6 p.895 ~ p.930
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Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches
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Kim Hyun-Ho
Kim Eun-Young
Lee In-Goo
Bae Bong-Sung
Park Min-Su
Nam Ho-Jung
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Abstract
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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.
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KEYWORD
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drug discovery, artificial intelligence, data-driven, machine learning
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