ANTIPASTI 1.2
Deep Learning model that predicts the binding affinity of antibodies from their three-dimensional structure.

# ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning

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ANTIPASTI (ANTIbody Predictor of Affinity from STructural Information) is a Python Deep Learning method that predicts the binding affinity of antibodies from their three-dimensional structure.

## Installation 

### Through PyPI

ANTIPASTI releases are distributed through the Python Package Index (PyPI). To install the latest version use `pip`:

```bash
pip install antipasti
```

### Through Anaconda
We provide an Anaconda environment that satisfies all the dependencies in `antipasti-env.yml`. 
```bash
git clone https://github.com/kevinmicha/ANTIPASTI
cd ANTIPASTI
conda env create -f antipasti-env.yml
conda activate antipasti-env
pip install .
```

Next, you can run the tests to make sure your installation is working correctly.

```bash
# While still in the ANTIPASTI directory:
pytest . 
```

### Manually handling the dependencies
If you want to use an existing environment, just omit the Anaconda commands above:
```bash
git clone https://github.com/kevinmicha/ANTIPASTI
cd ANTIPASTI
pip install .
```

or if you need to install it for your user only: 
```bash
python setup.py install --user 
```

## Requirements 

ANTIPASTI requires the following Python packages: 
* `adabelief-pytorch`
* `biopython`
* `matplotlib`
* `numpy`
* `opencv-python`
* `optuna`
* `pandas`
* `scikit-learn`
* `torch`
* `torchmetrics`
* `umap-learn`
    


## Example Notebooks and Documentation
The full documentation can be found [here](https://kevinmicha.github.io/ANTIPASTI/). 

Example notebooks are located in the [notebooks](https://github.com/kevinmicha/ANTIPASTI/tree/main/notebooks) folder:
* [[Tutorial] Training ANTIPASTI](https://github.com/kevinmicha/ANTIPASTI/blob/main/notebooks/%5BTutorial%5D%20Training%20ANTIPASTI.ipynb)
* [[Tutorial] Predicting affinity using ANTIPASTI](https://github.com/kevinmicha/ANTIPASTI/blob/main/notebooks/%5BTutorial%5D%20Predicting%20affinity%20using%20ANTIPASTI.ipynb)
* [[Tutorial] Explaining binding affinity with ANTIPASTI](https://github.com/kevinmicha/ANTIPASTI/blob/main/notebooks/%5BTutorial%5D%20Explaining%20binding%20affinity%20with%20ANTIPASTI.ipynb)
* [[Tutorial] Combining AlphaFold and ANTIPASTI](https://github.com/kevinmicha/ANTIPASTI/blob/main/notebooks/%5BTutorial%5D%20Combining%20AlphaFold%20and%20ANTIPASTI.ipynb)

You can download normal mode correlation maps computed at atomistic scale (instead of residue scale) [here](https://drive.google.com/drive/folders/1E8-GwQq9GHBE0A6r2t8dblAzP7qf0seQ?usp=sharing) and place them in `data/dccm_maps_full_ags_all_aa`.

## Attribution

If you use this code, please cite the [paper](https://kevinmicha.github.io/ANTIPASTI/citing.html) indicated in the documentation.