5. Conclusions
In this paper, we developed a novel chemical reaction superiority
evaluation index based on the information from the reaction database
using deep learning (DL) methods. For the modeling of RSscore, a new
type of chemical reaction graph descriptor with atom mapping
relationship was constructed, which provides sufficient chemical
reaction information and significantly improved the model classification
ability. Label smoothing was also employed to reduce the overfitting of
the model and enable differentiation of reaction superiority within the
same categories. Contrastive learning pre-training and supervised
learning fine-tuning method were used to improve the generality of the
model and the accuracy of classification.
The effect of different message passing methods was investigated and the
AIR residual on the GNN model is used to generate the RSscore. It proves
that the GINE message passing methods combined with AIR residual
demonstrate the best outcomes on classification. Additionally, the data
distribution of the entire dataset is analyzed and visualized using UMAP
dimensionality reduction, which showed that the developed model
effectively distinguishes reaction superiority and generates a robust
evaluation metric. The effectiveness of this RSscore provides a crucial
evaluation index for the computer-aided synthesis planning.