Molecular graph enhanced transformer for retrosynthesis prediction

With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 457; pp. 193 - 202
Main Authors Mao, Kelong, Xiao, Xi, Xu, Tingyang, Rong, Yu, Huang, Junzhou, Zhao, Peilin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 07.10.2021
Subjects
Graph neural network
Molecular pattern
Retrosynthesis
Transformer
Retrosynthesis
Transformer
Molecular pattern
Graph neural network
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Summary:With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2021.06.037