Artificial Intelligence in Pharmaceutical Sciences
Mingkun Lu , Jiayi Yin , Qi Zhu , Gaole Lin , Minjie Mou , Fuyao Liu , Ziqi Pan , Nanxin You , Xichen Lian , Fengcheng Li , Hongning Zhang , Lingyan Zheng , Wei Zhang , Hanyu Zhang , Zihao Shen , Zhen Gu , Honglin Li , Feng Zhu
Engineering ›› 2023, Vol. 27 ›› Issue (8) : 37 -69.
Artificial Intelligence in Pharmaceutical Sciences
Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.
Artificial intelligence / Machine learning / Deep learning / Target identification / Target discovery / Drug design / Drug discovery
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