# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 4 Results  
### Ablation Experiment  
To evaluate the effectiveness of different input features in the model, we performed ablation studies on DLERKm and its variants to assess prediction performances. By removing the molecular set fingerprint features extracted by molecular set feature extraction module, we obtained a variant of DLERKm, denoted as DLERKm-v1. Additionally, by removing the enzymatic reaction features extracted by RXNFP, we obtained another variant of DLERKm, called DLERKm-v2. The architectures of these two variants are shown in Supplementary Figure S5. Specifically, we used the same hyperparameters to train these models and compared the prediction performances of DLERKm, DLERKm-v1, and DLERKm-v2 on the same test set.  
To evaluate the importance of different input features, we compared the prediction performances of DLERKm with its variants. The values of RMSE, MAE, R\({}^{2}\), PCC metrics for all these models on the test set are shown in Figure 4. The results show that DLERKm, which incorporates enzyme sequence features, enzyme reaction features, and molecular set features as input, outperforms the other two variant models (DLERKm-v1 and DLERKm-v2) in terms of evaluation metrics. As shown in the Table 3, the relative improvements of DLERKm over DLERKm-v2 in terms of RMSE, R\({}^{2}\) and PCC are 0.2%, 0.7%, and 0.3%, respectively. Furthermore, DLERKm-v2 shows improvements over DLERKm-v1 by 4.2%, 8.9%, 6.7%, and 3.3% in terms of RMSE, MAE, R\({}^{2}\), and PCC. Moreover, we compared the computation scales of DLERKm and its variants in terms of the training time and GPU memory usage, where more details can be found in Supplementary Table S1. The results illustrate that the computation cost of DLERKm and its variants are almost the same, which means that introducing molecular set fingerprint features and enzymatic reaction features can improve the model prediction performances without consuming additional computation resources.  
Moreover, to show the interpretability of DLERKm, we investigate the importance of these input features, where the visualization results are shown in Figure 5. The results indicate that products and reaction features are equally important as enzyme sequence and substrate features for Km prediction task, thereby explaining the effectiveness of these features.  
In summary, the above results show that extracting enzymatic reaction features (including product information) is crucial for predicting Km values. Specifically, the interpretability analysis further demonstrates that considering changes in molecular fingerprints (DERF) in enzymatic reactions is the main reason for improving the model prediction performances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **R\({}^{2}\)** & **PCC** \\ \hline DLERKm-v1 & 0.8067 & 0.5969 & 0.5520 & 0.7430 \\ DLERKm-v2 & 0.7728 & **0.5438** & 0.5892 & 0.7676 \\ DLERKm & **0.7711** & 0.5454 & **0.5938** & **0.7706** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metric values of prediction results for DLERKm, DLERKm-v1, and DLERKm-v2.