Update README.md

README.md

@@ -1,17 +1,32 @@
 ---
 license: mit
+language:
+- en
+tags: 
+- biology 
+- esm 
+- protein 
+- protein-structure-prediction 
+- structure-prediction 
+- protein-design 
+- 3d-structure 
+- confidence-estimation 
+- molecular-dynamics 
+- transformers
 ---
 # Model Card for ESMFold2-Fast
 
 ## Model Details
 
-ESMFold2 is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model representations with a diffusion-based structure prediction architecture inspired by AlphaFold3. 
+ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The model achieves unprecedented success rates for protein binder and antibody generation, validated through biophysical and functional characterization.
 
 The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for enhanced accuracy on challenging targets. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMFold2 is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids.
 
 ESMFold2-Fast is an Inference optimized single-sequence structure prediction model and is not MSA conditioned.
 
-For additional information, visit the [Biohub Platform](https://biohub.ai) for no-code tools, step-by-step tutorial notebooks, and detailed information on the models.
+To run this model with the Biohub Platform API, visit the [Biohub Platform](https://biohub.ai/). 
+
+Read more about ESMFold2 in our paper [here](https://biohub.ai/papers/esmc.pdf).
 
 ### System Requirements
 
@@ -22,6 +37,10 @@ For additional information, visit the [Biohub Platform](https://biohub.ai) for n
 
 Coming soon
 
+### Citation
+
+Coming soon
+
 ### Model Architecture
 
 The architecture consists of an input embedder, which includes the ESMC language model embeddings, a pairwise folding block trunk, an atom diffusion module, an optional MSA encoder, and a confidence head.
@@ -157,9 +176,6 @@ Refer to the [paper](https://biohub.ai/papers/esmc.pdf) for details on additiona
 
 Do not use the model for the following purposes:
 
-- Clinical diagnosis or treatment recommendations.  
-- Any use that violates applicable laws, regulations (including trade compliance laws), or third-party rights such as privacy or intellectual property rights.  
-- Any use that is prohibited by the [model license](https://github.com/Biohub/esm/blob/main/LICENSE.md).  
 - Any use that is prohibited by the [Acceptable Use Policy](https://biohub.org/acceptable-use-policy/).
 
 ### Caveats and Recommendations