Artificial intelligence-based drug development: current progress and future challenges

YU Zehao; ZHANG Leiming; ZHANG Mengna; DAI Zhiqi; PENG Chengbin; ZHENG Siming

doi:10.11665/j.issn.1000-5048.2023041003

Journal of China Pharmaceutical University > 2023 > 54(3): 282-293. > DOI: 10.11665/j.issn.1000-5048.2023041003

YU Zehao, ZHANG Leiming, ZHANG Mengna, DAI Zhiqi, PENG Chengbin, ZHENG Siming. Artificial intelligence-based drug development: current progress and future challenges[J]. Journal of China Pharmaceutical University, 2023, 54(3): 282-293. DOI: 10.11665/j.issn.1000-5048.2023041003

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Artificial intelligence-based drug development: current progress and future challenges

1.
Health Science Center, Ningbo University, Ningbo 315211
2.
The First Affiliated Hospital of Ningbo University, Ningbo 315000
3.
College of Information Science and Engineering, Ningbo University, Ningbo 315211, China

Funds: This study was supported by the TCM Science and Technology Plan Project of Zhejiang Province (No.2022ZB323); the Medical and Health Science and Technology Plan Project of Zhejiang Province (No.2022KY1114); and the Natural Science Foundation of Ningbo (No.2021J268)

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Received Date: April 09, 2023
Revised Date: June 11, 2023

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Abstract

Abstract

In recent years, artificial intelligence (AI) has been widely applied in the field of drug discovery and development.In particular, natural language processing technology has been significantly improved after the emergence of the pre-training model.On this basis, the introduction of graph neural network has also made drug development more accurate and efficient.In order to help drug developers more systematically and comprehensively understand the application of artificial intelligence in drug discovery, this article introduces cutting-edge algorithms in AI, and elaborates on the various applications of AI in drug development, including drug small molecule design, virtual screening, drug repurposing, and drug property prediction, finally discusses the opportunities and challenges of AI in future drug development.
- artificial intelligence,
- expert system,
- machine learning,
- natural language processing,
- graph neural network,
- drug design,
- drug development

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References (60)

References

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