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README.md

@@ -17,7 +17,7 @@ tags:
 - transformers
 ---
 
-# Model Card for ESMFold2
+# ESMFold2
 
 ## Model Details
 
@@ -33,8 +33,8 @@ Read more about ESMFold2 in our paper [here](https://biohub.ai/papers/esm_protei
 
 | Model | MSA Conditioning | Description | Data Cutoff |
 | :---- | :---- | :---- | :---- |
-| [esmfold2](https://huggingface.co/biohub/esmfold2) | Yes | Large model, capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets | Sept 2021 |
-| [esmfold2-fast](https://huggingface.co/biohub/esmfold2-fast) | No | Inference optimized single-sequence structure prediction model | Sept 2021 |
+| [ESMFold2](https://huggingface.co/biohub/ESMFold2) | Yes | Large model, capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets | Sept 2021 |
+| [ESMFold2-Fast](https://huggingface.co/biohub/ESMFold2-Fast) | No | Inference optimized single-sequence structure prediction model | Sept 2021 |
 
 ## Performance Metrics
 
@@ -177,13 +177,6 @@ Informed by our risk assessments, we are releasing the source code and model wei
 
 ## Biases and Limitations
 
-### Potential Biases
-
-- The model may reflect biases present in the training data.
--
-
-### Limitations
-
 - **Dataset biases**: The model may reflect biases present in the training data (PDB, AFDB), including over-representation of certain protein families, experimental conditions, or structural classes. Performance may vary for underrepresented protein types.
 - **Dataset limitations:** PDB historically lacks comprehensive data on protein conformations, post-translational modifications, disordered regions, etc. Like all other structure prediction models trained on the PDB, performance may degrade on other biomolecules.
 - **Computational demand:** Highest accuracy structure predictions require scaling inference time compute. Predictions made with reduced inference parameters may lead to suboptimal performance.
@@ -205,7 +198,27 @@ Should you have any security or privacy issues or questions related to this mode
 
 ### Citation
 
-Coming soon.
+```
+@misc{candido2026language,
+  title  = {Language Modeling Materializes a World Model of Protein Biology},
+  author = {Candido, Salvatore and Hayes, Thomas and Derry, Alexander and Rao, Roshan
+            and Lin, Zeming and Verkuil, Robert and Wu, Bryan and Lee, Jin Sub
+            and Bruguera, Elise S. and Keval, Jehan A. and Kopylov, Mykhailo
+            and Pak, John E. and Wu, Wesley and Thomas, Neil and Mataraso, Samson
+            and Hsu, Alvin and Trotman-Grant, Ashton C. and Fatras, Kilian
+            and dos Santos Costa, Allan and Badkundri, Rohil and Ak{\i}n, Halil
+            and Oktay, Deniz and Deaton, Jonathan and Montabana, Elizabeth
+            and Sitwala, Hrishita and Yu, Yue and Wiggert, Marius
+            and Carlin, Dylan Alexander and Goering, Anthony W. and Blazejewski, Tomasz
+            and Sandora, McCullen and Hla, Michael and Jia, Tina Z.
+            and Kloker, Leon H. and Sofroniew, Nicholas J. and Uehara, Masatoshi
+            and Pannu, Jassi and Bachas, Sharrol and Liu, Daniel S.
+            and Sercu, Tom and Rives, Alexander},
+  year   = {2026},
+  url    = {https://biohub.ai/papers/esm_protein.pdf},
+  note   = {Preprint}
+}
+```
 
 ## Acknowledgements