Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, People's Republic of China.
College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201203, People's Republic of China.
Sci Rep. 2022 Oct 12;12(1):17098. doi: 10.1038/s41598-022-21524-6.
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.
为了提高数据驱动的反应预测模型的性能，我们提出了一种智能策略，使用可用数据进行反应产物预测，并使用虚假数据扩充来增加样本量。在这项研究中，我们创建了虚假数据集，并对原始数据进行扩充，以构建虚拟训练模型。虚假反应数据集是通过替换某些官能团来创建的，即在数据分析策略中，将具有修饰官能团的虚假数据作为化合物，以增加反应预测的数据量。该方法在五个不同的反应中进行了测试，结果表明，与其他相关技术相比，该方法具有更高的模型预测能力。此外，我们在不同的模型中评估了该方法，证实了虚拟数据扩充的通用性。总之，虚拟数据扩充可以作为解决数据不足问题的有效措施，显著提高反应预测的性能。
