逆向合成规划是现代有机合成化学中合成路线设计的重要基础。合成化学发展至今，化学家们积累了大量的反应数据。自有机合成大师E。J。Corey将逆合成分析法与计算机结合提出LHASA(logic and heuristics applied to synthetic analysis)起，计算机根据反应数据自主学习并给出逆向合成路线成了化学家的愿景之一。近年来，基于数据驱动的研究范式不断发展，大量深度学习模型被提出并在逆向合成规划中取得了初步的成功，然而该类模型仍然存在高质量数据集稀缺、软硬件结合不佳、领域知识嵌入与发现困难等问题。通过深度学习实现逆向合成路线规划有待深入研究。
Recent Advances of the Application of Deep Learning for the Retro-synthesis Planning of Chemical Molecules
Retro-synthetic planning stands as a fundamental cornerstone in the design of synthetic routes within modern synthetic organic chemistry.Over the years,chemists have compiled an extensive database of reaction data.Ever since the pioneering work of E.J.Corey,who combined the concept of retro-synthetic analysis with computer algorithms to create LHASA(logic and heuristics applied to synthetic analysis),the vision of comput-ers autonomously learning and proposing retro-synthetic pathways based on reaction data has been a long-standing aspiration among chemists.In recent years,with the evolving data-driven research paradigm,numer-ous deep learning models have been proposed and have achieved preliminary success in retro-synthetic planning.Despite these advancements,the models still confront several challenges,including scarcity of high-quality data-sets,suboptimal integration of software and hardware,and difficulties in embedding and discovering domain-specific knowledge.Therefore,deepening the research to realize retro-synthetic route planning through deep learning remains an imperative endeavor.
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