To maintain the integrity of kinases and kinase family relationships while managing the dimensionality of AlphaFold2 and ESM2 embeddings, we trained a deep convolutional autoencoder (CAE). The CAE treated each embedding as a single-channel array, allowing convolutional blocks to capture specific patterns within the embeddings. The architecture featured an encoder with multiple convolutional layers -each followed by batch-normalization and max-pooling to reduce and stabilize the input dimensions. After passing through the convolutional layers, the output was flattened and then passed through a dense layer to obtain a latent embedding. A decoder then reconstructed the input from the latent embedding using a symmetrical setup to the encoder. The training loss combined mean-squared error (for embedding reconstruction accuracy) and crossentropy (for kinase family classification accuracy), ensuring the latent space accurately represented the clustering of kinase families while retaining reconstructive details. Specific implementation details are provided in Table .