Single-step Retrosynthesis via Reaction Center and Leaving Groups Prediction

Jiajun Zhu, Binjie Hong, Zixun Lan, Fei Ma*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Retrosynthesis aims to break down desired molecules into accessible building blocks in a systematic manner. However, current template-based retrosynthesis approaches face limitations due to a fixed set of training templates, hindering their ability to discover new chemical reactions. To overcome this challenge, we present a novel retrosynthesis prediction framework that can generate new chemical templates beyond the training set. This innovative approach has demonstrated superior performance compared to previous template-based methods. Additionally, we propose a method to improve atomic mapping accuracy by matching the maximum common subgraph of the reaction center and leaving group in the retrosynthetic template. This enhancement effectively increases the precision of atomic mapping settings.

Original languageEnglish
Title of host publicationProceedings - 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023
EditorsXiaoMing Zhao, Qingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350330755
DOIs
Publication statusPublished - 2023
Event16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023 - Taizhou, China
Duration: 28 Oct 202330 Oct 2023

Publication series

NameProceedings - 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

Conference

Conference16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023
Country/TerritoryChina
CityTaizhou
Period28/10/2330/10/23

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