Artificial Intelligence for Retrosynthesis Prediction
Name
1-s2.0-S2095809922005665-main.pdf
Description
Published version
Size
2.78 MB
Format
Adobe PDF
Checksum (MD5)
4c594e9ceea174f38a2a031ada2db0e2
Author(s)
Jiang, Yinjie
Yu, Yemin
Kong, Ming
Mei, Yu
Yuan, Luotian
Huang, Zhengxing
Kuang, Kun
Wang, Zhihua
Yao, Huaxiu
Zou, James
Date Issued
June 2023
Journal
Engineering
Publisher
Elsevier BV
Citation
Jiang, Yinjie, Yu, Yemin, Kong, Ming, Mei, Yu, Yuan, Luotian et al. 2023. "Artificial Intelligence for Retrosynthesis Prediction." Engineering, 25.
Version
Final published version
Abstract
In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction performed by chemists and by rule-based expert systems, AI-driven retrosynthesis prediction automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions and retrosynthesis routes. This provides an opportunity to address many conventional challenges, including heavy reliance on extensive expertise, the sub-optimality of routes, and prohibitive computational cost. This review describes the current landscape of AI-driven retrosynthesis prediction. We first discuss formal definitions of the retrosynthesis problem and review the outstanding research challenges therein. We then review the related AI techniques and recent progress that enable retrosynthesis prediction. Moreover, we propose a novel landscape that provides a comprehensive categorization of different retrosynthesis prediction components and survey how AI reshapes each component. We conclude by discussing promising areas for future research.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.eng.2022.04.021
