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A transformer model for retrosynthesis.
Lect. Notes Comput. Sc. 11731 LNCS, 817-830 (2019)
We describe a Transformer model for a retrosynthetic reaction prediction task. The model is trained on 45 033 experimental reaction examples extracted from USA patents. It can successfully predict the reactants set for 42.7% of cases on the external test set. During the training procedure, we applied different learning rate schedules and snapshot learning. These techniques can prevent overfitting and thus can be a reason to get rid of internal validation dataset that is advantageous for deep models with millions of parameters. We thoroughly investigated different approaches to train Transformer models and found that snapshot learning with averaging weights on learning rates minima works best. While decoding the model output probabilities there is a strong influence of the temperature that improves at $$\text {T}=1.3$$ the accuracy of models up to 1–2%.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Character-based Models ; Computer Aided Synthesis Planning ; Retrosynthesis Prediction ; Transformer
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Quellenangaben
Volume: 11731 LNCS,
Pages: 817-830
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute of Structural Biology (STB)