A comprehensive survey of AI-based retrosynthesis planning: Datasets, models, and tools

Retrosynthetic planning has long been recognized as a central challenge in organic synthesis, and in recent years it has increasingly become a key domain for the integration of artificial intelligence (AI) methodologies. This review provides a systematic overview of recent advances across retrosynthesis planning, including models, datasets, evaluation metrics, and practical platforms and tools that have shaped the field. Beyond the classical single-step retrosynthesis prediction (reactants recommendation), the work particularly emphasizes on two emerging aspects: (i) the prediction of reaction conditions and yields, which is critical for bridging theoretical retrosynthesis with experimental feasibility, and (ii) the application of large language models (LLMs) to retrosynthetic prediction, a rapidly growing direction which emphasizes interactive problem solving and the breaking down of disciplinary boundaries. Moreover, the survey highlights both the current achievements and the persistent challenges of intelligent retrosynthesis. Finally, it outlines future opportunities that may drive the next wave of innovation in AI-assisted synthesis planning.