Artificial Intelligence in Pharmaceutical Sciences

• Published in: Engineering (Beijing, China) Vol. 27; pp. 37 - 69
• Main Authors: Lu, Mingkun, Yin, Jiayi, Zhu, Qi, Lin, Gaole, Mou, Minjie, Liu, Fuyao, Pan, Ziqi, You, Nanxin, Lian, Xichen, Li, Fengcheng, Zhang, Hongning, Zheng, Lingyan, Zhang, Wei, Zhang, Hanyu, Shen, Zihao, Gu, Zhen, Li, Honglin, Zhu, Feng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023; Elsevier
• Subjects: Artificial intelligence; Deep learning; Drug design; Drug discovery; Machine learning; Target discovery; Target identification; Deep learning; Target identification; Target discovery; Machine learning; Drug design; Drug discovery; Artificial intelligence
• ISSN: 2095-8099
• DOI: 10.1016/j.eng.2023.01.014

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.