Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks

Yu Wang, Chao Pang, Yuzhe Wang, Junru Jin, Jingjie Zhang, Xiangxiang Zeng, Ran Su, Quan Zou () and Leyi Wei ()
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Yu Wang: Shandong University
Chao Pang: Shandong University
Yuzhe Wang: Shandong University
Junru Jin: Shandong University
Jingjie Zhang: Shandong University
Xiangxiang Zeng: College of Computer Science and Electronic Engineering, Hunan University
Ran Su: Tianjin University
Quan Zou: University of Electronic Science and Technology of China
Leyi Wei: Shandong University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Automating retrosynthesis with artificial intelligence expedites organic chemistry research in digital laboratories. However, most existing deep-learning approaches are hard to explain, like a “black box” with few insights. Here, we propose RetroExplainer, formulizing the retrosynthesis task into a molecular assembly process, containing several retrosynthetic actions guided by deep learning. To guarantee a robust performance of our model, we propose three units: a multi-sense and multi-scale Graph Transformer, structure-aware contrastive learning, and dynamic adaptive multi-task learning. The results on 12 large-scale benchmark datasets demonstrate the effectiveness of RetroExplainer, which outperforms the state-of-the-art single-step retrosynthesis approaches. In addition, the molecular assembly process renders our model with good interpretability, allowing for transparent decision-making and quantitative attribution. When extended to multi-step retrosynthesis planning, RetroExplainer has identified 101 pathways, in which 86.9% of the single reactions correspond to those already reported in the literature. As a result, RetroExplainer is expected to offer valuable insights for reliable, high-throughput, and high-quality organic synthesis in drug development.

Date: 2023
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DOI: 10.1038/s41467-023-41698-5

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