Machine learning-assisted retrosynthesis planning:Current status and future prospects  

作　　者：Yixin Wei Leyu Shan Tong Qiu Diannan Lu Zheng Liu 

机构地区：[1]Department of Chemical Engineering,Tsinghua University,Beijing 100084,China [2]Beijing Key Laboratory of Industrial Big Data System and Application,Beijing 100084,China

出　　处：《Chinese Journal of Chemical Engineering》2025年第1期273-292,共20页中国化学工程学报(英文版)

基　　金：supported by the National Key Research and Development Program of China(2022ZD0117501).

摘　　要：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.

关 键 词：Retrosynthesis planning Machine learning Artificial intelligence Synthetic pathway CHEMOINFORMATICS 

分 类 号：O15[理学—数学]