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RSscore: Reaction Superiority Learned from Reaction Mapping Hypergraph
  • Chenyang Xu,
  • Lijuan Guo,
  • Lei Zhang
Chenyang Xu
Dalian University of Technology
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Lijuan Guo
Dalian University of Technology
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Lei Zhang
Dalian University of Technology

Corresponding Author:[email protected]

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Abstract

The selection of chemical reactions is directly related to the quality of synthesis pathways, a reasonable reaction evaluation index plays a crucial role in the design and planning of synthesis pathways. Since the construction of traditional reaction evaluation indicators mostly rely on the structure of molecules rather than the reactions themselves, considering the impact of reaction agents poses a challenge for traditional evaluation indicators. In this study, we first propose a chemical reaction graph descriptor that includes the mapping relationship of atoms to effectively extract reaction features. Then, through pre-training using graph contrastive learning and fine-tuning through supervised learning, we establish a model for generating the probability of reaction superiority (RSscore). Finally, to validate the effectiveness of the current evaluation index, RSscore is applied in two applications: reaction evaluation and synthesis routes analysis, which proves that the RSscore provides an important agents-considered evaluation criterion for Computer-Aided Synthesis Planning (CASP).
17 Oct 2023Submitted to AIChE Journal
27 Oct 2023Assigned to Editor
27 Oct 2023Submission Checks Completed
27 Oct 2023Review(s) Completed, Editorial Evaluation Pending
31 Oct 2023Reviewer(s) Assigned