The official implementation of Improving Antibody Humanness Prediction using Patent Data

1. Model
2. Environment
3. Configuration
4. Training and Evaluation
5. Structure of the repo
6. Results
7. Experiment tracking
8. Citing the paper
9. Citing this repo

Pre-training	Fine-tuning

We used Python 3.7 for our experiments. The environment can be set up by following three steps:

pip install pipenv             # To install pipenv if you don't have it already
pipenv install --skip-lock     # To install required packages. 
pipenv shell                   # To activate virtual env

If the second step results in issues, you can install packages in Pipfile individually by using pip i.e. "pip install package_name".

There are two types of configuration files:

1. pad.yaml         # Defines parameters and options for pre-training
2. humanness.yaml   # Defines parameters and options for fine-training

You can train and evaluate the model by using:

python selfpad_pretrain.py        # For pre-training
python selfpad_finetune.py        # For fine-tuning it for humanness
python selfpad_eval.py -ev test    # To compute humanness score for custome dataset, in this case it is test.csv. CSV file should have "VH", "VL" and/or "Label" columns

- selfpad_pretrain.py
- selfpad_finetune.py
- selfpad_eval.py

- src
    |-selfpad.py
    |-selfpad_humanness.py

- config
    |-pad.yaml
    |-humanness.yaml
    
- utils_common
    |-arguments.py
    |-utils.py
    |-tokenizer.py
    ...
    
- utils_pretrain
    |-load_data.py
    |-model_utils.py
    |-loss_functions.py
    ...
    
- utils_finetune
    |-load_data.py
    |-model_utils.py
    |-loss_functions.py
    ...
    
- data
    |-test.csv
    ...
    
- results
    |-pretraining
    |-humanness
    ...
    

Results at the end of training is saved under ./results directory. Results directory structure is as following:

- results
    |-task e.g. humanness, or pretraining
            |-evaluation
                |-clusters (for plotting t-SNE and PCA plots of embeddings)
            |-training
                |-model
                |-plots
                |-loss

You can save results of evaluations under "evaluation" folder.

You can turn on Weight and Biases (W&B) in the config file for logging

@article{ucar2024SelfPAD,
  title={Improving Antibody Humanness Prediction using Patent Data},
  author={Ucar, Talip and 
          Ramon, Aubin and 
          Oglic, Dino and 
          Croasdale-Wood, Rebecca and 
          Diethe, Tom and 
          Sormanni, Pietro},
  journal={arXiv preprint arXiv:2110.04361},
  year={2024}
}

If you use SelfPAD framework in your own studies, and work, please cite it by using the following:

@Misc{talip_ucar_2024_SelfPAD,
  author =   {Talip Ucar},
  title =    {{Improving Antibody Humanness Prediction using Patent Data}},
  howpublished = {\url{https://github.com/AstraZeneca/SelfPAD}},
  month        = January,
  year = {since 2024}
}