Issue 9, 2022

Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model

Abstract

Even in modern organic chemistry, predicting or proposing a reaction mechanism and speculating on reaction intermediates remains challenging. For example, it is challenging to predict the regioselectivity of radical addition in radical cascade cyclization, which finds wide application in life sciences and pharmaceutical industries. In this work, radical cascade cyclization is considered to demonstrate that Transformer, a sequence-to-sequence deep learning model, is capable of predicting the reaction intermediates. A major challenge is that the number of intermediates involved in the different reactions is variable. By defining “key intermediates”, this thorny problem was avoided. We curated a database of 874 chemical equations and corresponding 1748 key intermediates and used the dataset to fine-tune a model pretrained based on the USPTO dataset. The format of the dataset is very different between pretraining and fine-tuning. Correspondingly, the resulting Transformer model achieves remarkable accuracy in predicting the structures and stereochemistry of the key intermediates. The interpretability produced by attention weights of the resulting Transformer model shows a mindset similar to that of an experienced chemist. Hence, our study provides a novel approach to help chemists discover the mechanisms of organic reactions.

Graphical abstract: Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model

Supplementary files

Article information

Article type
Research Article
Submitted
05 Feb 2022
Accepted
16 Mar 2022
First published
17 Mar 2022

Org. Chem. Front., 2022,9, 2498-2508

Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model

J. Xu, Y. Zhang, J. Han, A. Su, H. Qiao, C. Zhang, J. Tang, X. Shen, B. Sun, W. Yu, S. Zhai, X. Wang, Y. Wu, W. Su and H. Duan, Org. Chem. Front., 2022, 9, 2498 DOI: 10.1039/D2QO00188H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements