Our initial analysis revealed no significant differences between the AlphaFold2, Prot-Bert, and ESM2 embeddings under initial conditions-where neither rebalancing nor permutation was applied-across various data splits, embedding modifications, and performance metrics. Table illustrates that embedding type did not significantly affect the F1-score as determined by a four-way ANOVA, and while the differences in AUROC were statistically significant, Tukey's HSD tests indicated no substantial variations between the embedding types that would suggest a preference (Supp. Table ). Furthermore, the noticeable differences in PFI primarily re-Figure : UMAP projections of assessed dimensionality reduction techniques. This figure illustrates the effects of mean-reduction on AlphaFold2 and ESM2 embeddings before and after applying a convolutional autoencoder (CAE).Initially, both original and trimmed embeddings underwent mean-reduction to facilitate model training, obscuring critical structural relationships between kinases (left two panels).To mitigate this information loss, a CAE was employed to reduce dimensions while ensuring that kinases' relationships in the latent space were preserved.