Retrosynthesis prediction with an iterative string editing model

Yuqiang Han, Xiaoyang Xu, Chang-Yu Hsieh, Keyan Ding, Hongxia Xu, Renjun Xu, Tingjun Hou (), Qiang Zhang () and Huajun Chen ()
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Yuqiang Han: Zhejiang University
Xiaoyang Xu: Zhejiang University
Chang-Yu Hsieh: Zhejiang University
Keyan Ding: Zhejiang University
Hongxia Xu: Zhejiang University
Renjun Xu: Zhejiang University
Tingjun Hou: Zhejiang University
Qiang Zhang: Zhejiang University
Huajun Chen: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K.

Date: 2024
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DOI: 10.1038/s41467-024-50617-1

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