mention companion spaces

app.py

@@ -30,7 +30,9 @@ with gr.Blocks(title="ESM2 Protein Embeddings") as demo:
     # ESM2 for candidate sequence filtering 🤖
                 
     Once one has generated de novo protein sequences using a tool like LigandMPNN, one must rank them to select promising candidates for experimental validation. One powerful approach is to use <a href="https://www.science.org/doi/10.1126/science.ade2574" target="_blank">protein language models like Meta's ESM2.</a>
-    These language models rely on a BERT-like architecture and a Masked Language Modeling (MLM) objective to learn rich representations of protein sequences. ESM can be used for two main purposes in the context of protein design:
+    These language models rely on a BERT-like architecture and a Masked Language Modeling (MLM) objective to learn rich representations of protein sequences. Note that this Space pairs well with the companion <a href="https://huggingface.co/spaces/hugging-science/RFdiffusion3" target="_blank">RFdiffusion3</a>, <a href="https://huggingface.co/spaces/hugging-science/LigandMPNN" target="_blank">LigandMPNN</a> and RosettaFold3 Spaces for a full de novo design pipeline!
+                
+    ESM is used for two main purposes:
     1. **Generating embeddings**: ESM's hidden layers creates high-dimensional representations of protein sequences that capture structural and functional information. 
         These embeddings can be used as input features for downstream machine learning models to predict function, properties or even for folding.
         Embeddings can also be used with dimensionality reduction techniques like t-SNE to visualize to identify clusters or compare against known proteins.