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

Yixin Wei; Leyu Shan; Tong Qiu; Diannan Lu; Zheng Liu

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

Updated：2026-01-06

- Machine learning-assisted retrosynthesis planning: Current status and future prospects
- Chinese Journal of Chemical Engineering Vol. 77, Issue 1, Pages: 273-292(2025)
- Affiliations：
  
  1. Department of Chemical Engineering, Tsinghua University,Beijing,China,100084
  2. Beijing Key Laboratory of Industrial Big Data System and Application,Beijing,China,100084
- Author bio：
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- DOI：
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- Published：2025
- Accepted：
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Yixin Wei, Leyu Shan, Tong Qiu, Diannan Lu, Zheng Liu. Machine learning-assisted retrosynthesis planning: Current status and future prospects[J]. Chinese Journal of Chemical Engineering, 2025, 77(1): 273-292.
DOI：

Yixin Wei, Leyu Shan, Tong Qiu, Diannan Lu, Zheng Liu. Machine learning-assisted retrosynthesis planning: Current status and future prospects[J]. Chinese Journal of Chemical Engineering, 2025, 77(1): 273-292. DOI：

摘要

Abstract

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.

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