Efficient Data Undersampling for Rule-Based Retrosynthetic Planning

Abstract

Computer-aided retrosynthetic planning for organic molecules, which is based on a large synthetic database, is a significant part of the recent development of an autonomous robotic chemist. As in other AI fields, however, the class imbalance problem in the dataset affects the prediction performance of retrosynthetic paths. Here, we demonstrate that applying undersampling methods to the imbalanced reaction dataset can improve the prediction of retrosynthetic rules for target molecules. We report improvements in the top-1 and top-10 prediction accuracies by 13.8% (13.1, 5.4%) and 8.8% (6.9, 2.4%) for the undersampling based on the similarity (random, dissimilarity) clustering of molecular structures of products, respectively. These results demonstrate the importance of a deep understanding of the statistical distribution, internal structure, and sampling for the training dataset. For practical application, the target-oriented undersampling method is proposed and confirmed by the improved prediction performance of 9.3 and 4.2% for top-1 and top-10 accuracies, respectively.