metadata
license: apache-2.0
datasets:
- jheuschkel/clustered-cds-dataset
language:
- en
pipeline_tag: fill-mask
tags:
- codon
- Codon
- biology
- synthetic
- dna
- mrna
- optimization
- codon-optimization
- codon-embedding
- codon-representation
- codon-language-model
- codon-language
misc:
- codon
Model Card for SynCodonLM
- This model is a replicate of that trained with species - token type ID, however, trained without any token type ID.
- This model is totally protein-agnostic, while the species-token type model may still have a small amount of spurious statistical focus.
Installation
git clone https://github.com/Boehringer-Ingelheim/SynCodonLM.git
cd SynCodonLM
pip install -r requirements.txt #maybe not neccesary depending on your env :)
Embedding a Coding DNA Sequence Using our Model Trained without Token Type ID
from SynCodonLM import CodonEmbeddings
model = CodonEmbeddings(model_name='jheuschkel/SynCodonLM-V2-NoTokenType') #this loads the model & tokenizer using our built-in functions
seq = 'ATGTCCACCGGGCGGTGA'
mean_pooled_embedding = model.get_mean_embedding(seq)
#returns --> tensor of shape [768]
raw_output = model.get_raw_embeddings(seq)
raw_embedding_final_layer = raw_output.hidden_states[-1] #treat this like a typical Hugging Face model dictionary based output!
#returns --> tensor of shape [batch size (1), sequence length, 768]
Citation
If you use this work, please cite:
@article{10.1093/nar/gkag166,
author = {Heuschkel, James and Kingsley, Laura and Pefaur, Noah and Nixon, Andrew and Cramer, Steven},
title = {Advancing codon language modeling with synonymous codon constrained masking},
journal = {Nucleic Acids Research},
volume = {54},
number = {5},
pages = {gkag166},
year = {2026},
month = {02},
abstract = {Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on six of seven benchmarks sensitive to DNA-level features, including messenger RNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.},
issn = {1362-4962},
doi = {10.1093/nar/gkag166},
url = {https://doi.org/10.1093/nar/gkag166},
eprint = {https://academic.oup.com/nar/article-pdf/54/5/gkag166/67103471/gkag166.pdf},
}
}