Features of DeepProtein
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DeepProtein is a Deep Learning Library and Benchmark for Protein Sequence Learning Toolkit (using PyTorch)
It allows very easy usage for non-computational domain researchers to be able to deal with protein data
using deep learning while facilitating deep learning method research in this topic by providing a flexible framework (with few lines of codes!) 
and baselines.
The Github repository is located `here <https://github.com/jiaqingxie/DeepProtein>`_.


Features
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* For computational researchers, 10+ powerful encodings for  proteins, ranging from  CNN, transformers to GNNs and graph transformers. 

* Realistic and user-friendly design:

  * Applications in Protein Property Prediction, Localization Prediction, Protein-Protein Interaction, Antigen Epitope Prediction, Antibody Paratope Prediction, Antibody Developability Prediction, and more.
  * easy monitoring of training process with detailed training metrics output such as test set figures (AUCs) and tables, also support early stopping.
  * various evaluation metrics: ROC-AUC, PR-AUC, F1 for binary task, MSE, R-squared, Concordance Index for regression task.
  * time reference for computational expensive encoding. 
  * PyTorch based, support CPU, GPU, Multi-GPUs.  

TODO
^^^^^^^^^^^^^^^^^^^^^^^^^^^
* List

  * Pretraining
  * Protein Structure Prediction 
  * Protein design
  * Combination of Models
  * LightWeight Data Processing with Fewer Lines of codes