Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the data-driven paradigm. Despite these thrilling developments, the field of artificial intelligence (AI) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between AI and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development.

近年来，人工智能与有机和聚合物合成的结合带来了变革性的影响。这种协同作用为合成化学中的一系列经典问题提供了创新和智能的解决方案。这些令人兴奋的进展包括分子性质的预测、多步逆合成路径规划、单步转化的结构-性能关系的阐明、聚合物结构与其功能之间的定量联系的建立、聚合过程的设计和优化、预测生物大分子的结构和序列研究，以及自动化、智能化合成平台。化学家现在可以以前所未有的精度和效率探索合成化学，在数据驱动的范式下创造新的反应、催化剂和聚合物材料。尽管取得了这些令人兴奋的发展，人工智能（AI）合成化学领域仍处于起步阶段，在数据开放性、模型可解释性以及软硬件支持方面面临挑战和限制。本文旨在概述人工智能与合成化学交叉领域的当前进展、主要挑战以及未来的发展建议。希望本文的概述能让读者对这一新兴领域有一个全面的了解，启发和促进进一步的科学研究和发展。