In recent years, the rapid advancements in computer science have spurred the development of various cutting-edge intelligent algorithms. Among these, the transformer, which is built upon a multi-head attention mechanism, is one of the most prominent AI models. The advent of such algorithms has significantly advanced retrosynthesis prediction, though challenges remain in chemical interpretability and real-world deployment. Unlike traditional models, AI-based retrosynthesis prediction systems can automatically extract chemical knowledge from vast datasets to forecast retrosynthesis pathways. This review provides a comprehensive overview of modern intelligent algorithms applied to retrosynthesis prediction, with a particular focus on artificial intelligence techniques. We begin by discussing key deep learning models, then explore available chemical reaction datasets and molecular representations. The discussion extends to the latest state-of-the art in AI-assisted retrosynthesis models, including template-based, template-free, and semi-template-based approaches. Finally, we compare these models across various classifications, highlighting several challenges and limitations of current methods, and suggesting promising directions for future research.
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