Several deep learning models employ various protein embeddings, processing them through feed-forward or convolutional layers before prediction. However, their ability to learn complex, non-convex functions may result in identifying biases in the underlying dataset rather than true meaningful relationships between the inputs and outputs 50 . To counteract this, not only is a well-considered train-test split essential, but developing a latent space that captures the subtle differences/nuances between proteins could significantly enhance the modeling of bioactivity. Lin et al. demonstrated this concept by developing an encoder-quantizationdecoder model that constructs an amino acid vocabulary (ProTokens), enabling rapid structural comparisons consistent with physical constraints and pairwise alignments. Similarly, employing conformational subsampling techniques within AlphaFold2 could allow for the capture of varied protein conformations in embeddings, marking a significant step forward in refining protein representations.