readme: ---
  # language: protein
  tags:
  - protein language model
  datasets:
  - ProteinKG25
  widget:
  - text: "D L I P T S S K L V V [MASK] D T S L Q V K K A F F A L V T"
  
  ---
  	
  # OntoProtein model
  Pretrained model on protein sequences using masked language modeling (MLM) and knowledge embedding (KE) objective objective. It was introduced in [this paper](https://openreview.net/pdf?id=yfe1VMYAXa4) and first released in [this repository](https://github.com/zjunlp/OntoProtein). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
  
  ## Model description
  OntoProtein is the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training.
  
  ### BibTeX entry and citation info
  ```bibtex
  @article{zhang2022ontoprotein,
    title={OntoProtein: Protein Pretraining With Gene Ontology Embedding},
    author={Zhang, Ningyu and Bi, Zhen and Liang, Xiaozhuan and Cheng, Siyuan and Hong, Haosen and Deng, Shumin and Lian, Jiazhang and Zhang, Qiang and Chen, Huajun},
    journal={arXiv preprint arXiv:2201.11147},
    year={2022}
  }
  ```
  