人工智能在药物再利用治疗新型冠状病毒感染研究中的应用及问题分析

In recent years, drug repurposing has emerged as an effective strategy for identifying potential treatments for the outbreak of the Corona Virus Disease 2019 (COVID-19). Artificial intelligence (Al) has been widely employed in the field of drug repurposing, enabling rapid computation and screening of extensive drug databases. Based on different algorithm design principles, Al methodologies for drug repurposing in the context of COVID-19 can be categorized into three types:(1) network-based models, which emphasize the identification of associations between drugs and diseases to reveal potential therapeutic mechanisms; (2) structure-based methods, which employ the analysis of structural interactions between drugs and targets for precise screening; and (3) machine learning/deep learning approaches, which utilize multidimensional processing of complex nonlinear data for candidate drug prediction. Despite the significant role of Al in drug repurposing, the quality and quantity of data have a notable impact on the computational results of Al. Experimental studies alone cannot fully simulate the complex physiological environment of the human body, which may limit the precise validation of candidate drugs in the preclinical stage. Optimization of drugs originally indicated for other conditions may also affect the effectiveness of candidate drugs for COVID-19. Moreover, treatment timing and individual differences may influence clinical outcomes. This review provides an overview of the application and challenges of Ai in the field of drug repurposing for COVID-19 in order to provide reference for wider use of Al technology in COVID-19 treatment.