Virtual data augmentation method for reaction prediction

Xinyi Wu, Yun Zhang, Jiahui Yu, Chengyun Zhang, Haoran Qiao, Yejian Wu, Xinqiao Wang, Zhipeng Wu, Hongliang Duan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


To improve the performance of data-driven reaction prediction models, we propose an intelligent strategy for predicting reaction products using available data and increasing the sample size using fake data augmentation. In this research, fake data sets were created and augmented with raw data for constructing virtual training models. Fake reaction datasets were created by replacing some functional groups, i.e., in the data analysis strategy, the fake data as compounds with modified functional groups to increase the amount of data for reaction prediction. This approach was tested on five different reactions, and the results show improvements over other relevant techniques with increased model predictivity. Furthermore, we evaluated this method in different models, confirming the generality of virtual data augmentation. In summary, virtual data augmentation can be used as an effective measure to solve the problem of insufficient data and significantly improve the performance of reaction prediction.

Original languageEnglish
Article number17098
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2022
Externally publishedYes


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