计算机辅助药物设计（CADD）是一种计算方法，用于发现、开发和分析具有类似生化特性的药物和活性分子。分子模拟技术显著加速了药物研究，并降低了制造成本。它是一种优化的药物发现方法，极大地提高了新药开发过程的效率。本综述讨论了有效的抗癌抑制剂的分子模拟的发展，并通过介绍六个重要抗癌靶点的代表性类别的硅胶中研究的主要结果来追踪。作者从药物化学和人工智能的角度提供了对这一主题的观点，并指出了主要挑战和预测趋势。将CADD引入癌症治疗的目标是实现一种高效、准确、理想的方法，以高成功率识别出有效的药物候选物。然而，主要挑战是缺乏一个复杂的数据筛选机制来验证混合质量参考资料底部数据。因此，尽管算法、计算机能力和界面优化不断发展，但特定的数据筛选机制将成为未来的迫切和关键问题。

Computer-aided drug design (CADD) is a computational approach used to discover, develop, and analyze drugs and active molecules with similar biochemical properties. Molecular simulation technology has significantly accelerated drug research and reduced manufacturing costs. It is an optimized drug discovery method that greatly improves the efficiency of novel drug development processes.This review discusses the development of molecular simulations of effective cancer inhibitors and traces the main outcomes of in silico studies by introducing representative categories of six important anticancer targets. The authors provide views on this topic from the perspective of both medicinal chemistry and artificial intelligence, indicating the major challenges and predicting trends.The goal of introducing CADD into cancer treatment is to realize a highly efficient, accurate, and desired approach with a high success rate for identifying potent drug candidates. However, the major challenge is the lack of a sophisticated data-filtering mechanism to verify bottom data from mixed-quality references. Consequently, despite the continuous development of algorithms, computer power, and interface optimization, specific data filtering mechanisms will become an urgent and crucial issue in the future.