# A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions
## 5 Conclusions  
The Michaelis constant (Km) is an essential parameter of enzymatic reactions, which plays a critical role in the quantitative analysis of bioengineering. The determination of Km values through biological experiments is time-consuming and resource-intensive, and such experiments are also greatly influenced by external environmental factors. Although deep learning-based Km prediction models have addressed the above issues to some extent, the accuracy of these methods in predicting Km values for complex enzymatic reactions is limited due to the lack of product information features. In this paper, we proposed a new deep learning model named DLERKm to predict the Michaelis constant (Km) values based on enzymatic reactions. To construct a dataset of enzymatic reactions including product information for predicting Km, we collected enzymatic reaction samples and enzyme sequence samples from Sabio-RK and UniProt databases. Through data processing, we obtained a benchmark dataset consisting of 10,122 samples, which was further divided into training and test subsets based on the ratio of 8:2. Specifically, the ratio of wild-type enzymatic reactions to mutant enzymatic reactions in the test set is consistent with the ratio in the benchmark dataset. DLERKm consists of enzyme sequence feature extraction module, enzymatic reaction feature extraction module, molecular set feature extraction module, and downstream prediction module. The enzyme sequence feature and enzymatic reaction feature extraction modules leverage ESM-2 and RXNFP to extract enzyme sequence and enzymatic reaction features, respectively. The molecular set feature extraction module uses the molecular fingerprints of products and substrates to extract the features of enzymatic reaction fingerprints. The downstream prediction module further refines the above features by using a channel attention mechanism and concatenates them with an FFL to predict Km values. By conducting ablation experiments and interpretability analysis, we validated the contributions of different input features of DLERKm. The results of the ablation experiments and interpretability analysis also indicated the importance of considering the information of products in enzymatic reactions. Compared with UniKP, DLERKm achieved superior prediction performances, where the relative improvements across the four metrics (RMSE, MAE, PCC, and R\({}^{2}\)) are 16.3%, 16.5%, 27.7%, and 14.9%, respectively. We further validated the predictive performances of DLERKm on samples with different sequence similarities. The results illustrated that DLERKm maintained reliable prediction performances on samples with lower similarity. Moreover, by comparing the evaluation metric values on samples from different types of enzymatic reactions, the experimental results showed that DLERKm exhibited better prediction performances on wild-type and _H. sapiens_ enzymatic reaction samples. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15074017/s1](https://www.mdpi.com/article/10.3390/app15074017/s1)]([https://www.mdpi.com/article/10.3390/app15074017/s1](https://www.mdpi.com/article/10.3390/app15074017/s1)), Figures S1-S4: The detailed information of the downloaded datasets; Figure S5: The architecture of DLERKm-v1 and DLERKm-v2; Table S1: The average training time and GPU memory usage of DLERKm and its variants across datasets; Tables S2-S4: The prediction metric values across different ranges of true values [26,45]. Conceptualization, Y.L. and K.W. ; methodology, Y.L. and K.W. ; investigation, Y.L. and K.W. ; data curation, Y.L. ; software, Y.L. ; supervision, K.W. ; validation, Y.L. and K.W. ; writing--original draft preparation, Y.L. ; writing--review and editing, K.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China (62373166), and the China Postdoctoral Science Foundation (2022M711362).