从传统的细胞毒性化疗到小分子抗癌药物的靶向癌症治疗的转变提高了治疗效果。这种方法目前在癌症治疗中占主导地位，它有其优点。尽管监管部门批准了多种靶向分子用于临床，但低反应率和耐药性等挑战仍然存在。传统的药物发现方法既昂贵又耗时，需要更有效的方法。人工智能 (AI) 的兴起和大规模数据集的获取彻底改变了小分子癌症药物发现领域。机器学习 (ML)，特别是深度学习 (DL) 技术，通过分析大量基因组、蛋白质组和成像数据来揭示隐藏的模式和关系，能够快速识别和开发新型抗癌药物。在这篇综述中，作者探讨了人工智能驱动的药物发现史上的重要里程碑。他们还重点介绍了小分子癌症药物发现中的各种应用，概述了所面临的挑战，并为未来的研究提供了见解。大数据的出现使人工智能能够渗透到药物发现的几乎每个阶段并实现创新，改变了药物发现的格局。通过开发最先进的算法和模型进行肿瘤学研究。尽管存在数据质量、模型可解释性和技术限制方面的挑战，但进步有望在个性化和精准肿瘤学方面取得突破，彻底改变未来的癌症管理。

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.