Engineering (Jun 2023)

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

Journal volume & issue
Vol. 25
pp. 32 – 50

Abstract

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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.

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