Fig 4. Performance of the optimized models.

(a) MSE. (b) Coefficients of determination (R²). Values in (a) and (b) are calculated using the gradient boosting model with different inputs: substrate and enzyme information; substrate information only (GNN); and enzyme information only (domain content). Boxplots summarize the results of the 5-fold cross-validations on the training set; blue dots show the results on the test set. For comparison, we also show results on the test set from a naïve model using the mean of the K_M values in the training set for all predictions. (c) Scatter plot of log₁₀-transformed K_M values of the test set predicted with the gradient boosting model with substrate and enzyme information as inputs versus the experimental values downloaded from BRENDA. Red dots are for combinations where neither enzyme nor substrate were part of the training set. The data underlying the graphs shown in this figure can be found at https://github.com/AlexanderKroll/KM_prediction/tree/master/figures_data. GB, gradient boosting; GNN, graph neural network; MSE, mean squared error.