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@@ -17,7 +17,7 @@ This model is a fine-tuned version of ESM2-3B for paired antibody sequences (hea
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  **Key Features:**
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  - Trained on paired antibody sequences
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- - 15% WC followed by 50% CDR fine-tuning
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  - Input format: Heavy-Light chains separated by "-"
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  - Output: 2560-dimensional embeddings
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  - Optimized for antibody CDR region understanding
@@ -59,7 +59,7 @@ light_chain = (
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  paired_sequence = f"{heavy_chain}{SEP_TOKEN}{light_chain}"
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  # Tokenize
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- inputs = tokenizer(paired, return_tensors="pt", add_special_tokens=True)
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  # Extract embeddings
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  with torch.no_grad():
@@ -93,17 +93,34 @@ sequence = "EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMS...-DIQMTQSPSSLSASVGDRVTITCRASQSI
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  - **Sequence length:** Variable (up to ~1024 tokens including special tokens)
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  - **Format:** PyTorch tensor
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  ## Citation
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- If you use this model, please cite:
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  ```bibtex
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- @article{talaei2025preferential,
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- title={Preferential CDR masking in paired antibody language models improves binding affinity prediction},
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- author={Talaei, Mahtab and Walker, Kenji C. and Hao, Boran and Jolley, Eliot and Jin, Yeping and Kozakov, Dima and Misasi, John and Vajda, Sandor and Paschalidis, Ioannis Ch. and Joseph-McCarthy, Diane},
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- journal={bioRxiv},
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- year={2025},
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- doi={10.1101/2025.10.31.685149}
 
 
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  }
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  ```
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@@ -112,7 +129,7 @@ If you use this model, please cite:
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  - **Maintainer:** Network Optimization & Control (NOC) Lab
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  - **Email:** mtalaei@bu.edu
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  - **GitHub:** [https://github.com/noc-lab/AbCDR-ESM](https://github.com/noc-lab/AbCDR-ESM)
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- - **Paper:** [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2025.10.31.685149)
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  ## License
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  **Key Features:**
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  - Trained on paired antibody sequences
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+ - 15% whole-chain (WC) masking followed by 50% CDR masking during fine-tuning
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  - Input format: Heavy-Light chains separated by "-"
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  - Output: 2560-dimensional embeddings
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  - Optimized for antibody CDR region understanding
 
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  paired_sequence = f"{heavy_chain}{SEP_TOKEN}{light_chain}"
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  # Tokenize
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+ inputs = tokenizer(paired_sequence, return_tensors="pt", add_special_tokens=True)
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  # Extract embeddings
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  with torch.no_grad():
 
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  - **Sequence length:** Variable (up to ~1024 tokens including special tokens)
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  - **Format:** PyTorch tensor
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+ ## Publication
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+
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+ **Preferential CDR masking in paired antibody language models improves binding affinity prediction**
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+
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+ Mahtab Talaei, Kenji C. Walker, Boran Hao, Eliot Jolley, Yeping Jin, Dima Kozakov, John Misasi, Sandor Vajda, Ioannis Ch. Paschalidis, Diane Joseph-McCarthy.
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+
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+ *Communications AI & Computing* **1**, 7 (2026).
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+
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+ **DOI:** https://doi.org/10.1038/s44488-026-00010-2
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+
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+ **Published:** August 13, 2026
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+
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+ [Read the published article](https://www.nature.com/articles/s44488-026-00010-2)
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+
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  ## Citation
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+ If you use this model, please cite the published paper:
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  ```bibtex
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+ @article{Talaei2026,
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+ author = {Talaei, Mahtab and Walker, Kenji C. and Hao, Boran and Jolley, Eliot and Jin, Yeping and Kozakov, Dima and Misasi, John and Vajda, Sandor and Paschalidis, Ioannis Ch. and Joseph-McCarthy, Diane},
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+ title = {Preferential {CDR} masking in paired antibody language models improves binding affinity prediction},
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+ journal = {Communications AI \& Computing},
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+ volume = {1},
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+ pages = {7},
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+ year = {2026},
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+ doi = {10.1038/s44488-026-00010-2}
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  }
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  ```
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  - **Maintainer:** Network Optimization & Control (NOC) Lab
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  - **Email:** mtalaei@bu.edu
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  - **GitHub:** [https://github.com/noc-lab/AbCDR-ESM](https://github.com/noc-lab/AbCDR-ESM)
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+ - **Paper:** [Communications AI & Computing](https://doi.org/10.1038/s44488-026-00010-2)
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  ## License
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