The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.

药物开发成本与药物成功进入市场之间的反比关系导致需要创新的解决方案来克服这一迅速发展的问题。这个问题可能归因于几个因素，包括临床试验的提前终止、监管因素或早期药物开发过程中做出的决定。引入人工智能 (AI) 以加速和协助药物开发，从而使流程更便宜、更高效，最终提高了临床试验的成功率。本综述旨在展示和比较有助于自动化和提高药物开发成功率的人工智能技术的不同应用，特别是在新药物靶点识别和设计、药物重新定位、生物标志物识别、通过探索不同的疾病状况，对患者进行有效的分层。此外，还将重点介绍这些技术如何转化为临床应用。这种范式转变将导致人工智能在药物开发和发现过程中的自动化过程中的整合取得更大进步，从而实现未来精准和个性化医疗的可能性和现实。