Artificial Intelligence for Retrosynthesis Prediction
Yinjie Jiang,
Yemin Yu,
Ming Kong,
Yu Mei,
Luotian Yuan,
Zhengxing Huang,
Kun Kuang,
Zhihua Wang,
Huaxiu Yao,
James Zou,
Connor W. Coley,
Ying Wei
Affiliations
Yinjie Jiang
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Yemin Yu
Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China
Ming Kong
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Yu Mei
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Luotian Yuan
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Zhengxing Huang
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Kun Kuang
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China; Shanghai Artificial Intelligence Laboratory, Shanghai 201203, China
Zhihua Wang
Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 201203, China; Shanghai Artificial Intelligence Laboratory, Shanghai 201203, China
Huaxiu Yao
Department of Computer Science, Stanford University, Stanford, CA 94305–2004, USA
James Zou
Department of Computer Science, Stanford University, Stanford, CA 94305–2004, USA
Connor W. Coley
Department of Chemical Engineering & Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Ying Wei
Department of Computer Science, City University of Hong Kong, Hong Kong 999077, China; Corresponding author.
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.