As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

随着药物开发支出呈指数级增长，整个药物发现过程需要一场可持续的革命。由于人工智能（AI）正在引领第四次工业革命，因此AI可以被认为是不稳定药物研发的可行解决方案。一般来说，人工智能应用于计算机视觉和自然语言处理等数据充足的领域，但有很多努力通过应用人工智能来彻底改变现有的药物发现过程。这篇综述对人工智能引导的药物发现过程的最新研究趋势进行了全面、有条理的总结，包括目标识别、命中识别、ADMET 预测、先导优化和药物重新定位。本综述还总结了每个领域的主要数据来源。此外，还将对剩余的挑战和限制进行深入分析，并对上述每个领域的未来发展方向提出建议。