Synlett
DOI: 10.1055/a-1937-9113
cluster
Machine Learning and Artificial Intelligence in Chemical Synthesis

A Novel Application of a Generation Model in Foreseeing ‘Future’ Reactions

Lujing Cao
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Yejian Wu
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Yixin Zhuang
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Linan Xiong
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Zhajun Zhan
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Liefeng Ma
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
,
Hongliang Duan
a   College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. of China
b   State Key Laboratory of Drug Research, Shanghai Institute of Materia Medical (SIMM), Chinese Academy of Sciences, Shanghai, 201203, P. R. of China
› Author Affiliations
This project was supported by the National Natural Science Foundation of China, (No.81903438) and Natural Science Foundation of Zhejiang Province (LD22H300004).


Abstract

Deep learning is widely used in chemistry and can rival human chemists in certain scenarios. Inspired by molecule generation in new drug discovery, we present a deep-learning-based approach to reaction generation with the Trans-VAE model. To examine how exploratory and innovative the model is in reaction generation, we constructed the dataset by time splitting. We used the Michael addition reaction as a generation vehicle and took these reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after that date. We took 2010 and 2015 as time points for splitting the reported Michael addition reaction; among the generated reactions, 911 and 487 reactions were applied in the experiments after the respective split time points, accounting for 12.75% and 16.29% of all reported reactions after each time point. The generated results were in line with expectations and a large number of new, chemically feasible, Michael addition reactions were generated, which further demonstrated the ability of the Trans-VAE model to learn reaction rules. Our research provides a reference for the future discovery of novel reactions by using deep learning.

Supporting Information



Publication History

Received: 14 May 2022

Accepted after revision: 06 September 2022

Accepted Manuscript online:
06 September 2022

Article published online:
07 October 2022

© 2022. Thieme. All rights reserved

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