
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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〉 |