Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target compounds.In recent years,with the development of artificial intelligence(AI),espe...Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target compounds.In recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the field.In this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis planning.We first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway verification.Then,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest progress.Following that,we specifically discuss large language models in retrosynthesis planning.Finally,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.展开更多
基金supported by the National Key Research and Development Program of China(2022ZD0117501).
文摘Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target compounds.In recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the field.In this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis planning.We first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway verification.Then,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest progress.Following that,we specifically discuss large language models in retrosynthesis planning.Finally,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.
