深度学习在化学分子逆向合成路线规划中的应用进展OACHSSCDCSTPCD

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.