摘要
Drug discovery is a complex and highly systematic process encompassing multiple critical stages,including target identification,bioactive molecule discovery,preclinical research,clinical trials,regulatory review,post-marketing surveillance,and others[1].This process typically spans many years and is often accompanied by high failure rates and substantial resource consumption.In recent years,driven by large amounts of biomedical data,artificial intelligence(AI)has begun to reshape every stage of drug discovery[2].Particularly,by integrating diverse,high-dimensional datasets with powerful predictive and generative models.
