More recently, pLMs have become increasingly popular . These models are able to capture the relationships between protein residues by analyzing their context within the sequence, producing neural networks that generate meaningful residue-level representations. Known as embeddings, these high-dimensional vectors facilitate various machine learning applications . It was shown that amino acids can be clustered by biochemical and biophysical properties based on their embedding space positions . Unlike the molecular fingerprints discussed earlier, pLMs are strictly limited to the 20 standard amino acids and linear sequences. In our study, we evaluated the following pretrained pLMs: ProtT5-XL-UniRef5 (ProtT5) , esm1b_t33_650M_UR50S (ESM-1b) and esm2_t36_3B_UR50D (ESM-2) , all employing the transformer architecture and trained on UniRef50 to predict masked amino acids in a self-supervised manner. We averaged the generated residue representations to achieve fixed-size representations, enabling comparison across sequences of varying lengths.