Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

• Published in: Biotechnology and bioprocess engineering Vol. 25; no. 6; pp. 895 - 930
• Main Authors: Kim, Hyunho, Kim, Eunyoung, Lee, Ingoo, Bae, Bongsung, Park, Minsu, Nam, Hojung
• Format: Journal Article
• Language: English
• Published: Seoul The Korean Society for Biotechnology and Bioengineering 01.12.2020; Springer Nature B.V; 한국생물공학회
• Subjects: Artificial intelligence; bioprocessing; Biotechnology; Chemistry; Chemistry and Materials Science; Computer vision; Drug development; Drug discovery; drugs; Industrial and Production Engineering; Learning algorithms; Machine learning; Natural language processing; Optimization; prediction; R&D; Research & development; research and development; Review Paper; Target recognition; 생물공학; drug discovery; machine learning; data-driven; artificial intelligence
• ISSN: 1226-8372; 1976-3816
• DOI: 10.1007/s12257-020-0049-y

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.