DeepCocrystal

Welcome to DeepCocrystal, a predictive model that will help you select promising coformers for your co-crystallization trails, simply from the SMILES of your molecules.

Let's get started! ✨

Installation 🛠️

You first need to download this codebase. You can either click on the green button on the top-right corner of this page and download the codebase as a zip file or clone the repository with the following command, if you have git installed:

git clone https://github.com/molML/deep-cocrystal.git

We'll use conda to create a new environment for our codebase. If you haven't used conda before, we recommend you take a look at this tutorial before moving forward.

Otherwise, fire up a terminal in the (root) directory of the codebase and type the following commands:

conda create -n deepcocrystal_env python==3.10.13 
conda activate deepcocrystal_env 
conda install --file requirements.txt -c conda-forge  
python -m pip install .  # install this codebase -- make sure that you are in the root directory of the codebase

That's it! You have successfully installed our codebase.

Data Preparation and Model Training 💊

Before using DeepCocrystal, you need to format your data into a .csv file with three columns: SMILES1, SMILES2, and cocrystallization, which contains the SMILES of the active pharmaceutical ingredients (APIs), coformers, and the observed cocrystallization output, respectively. You can see train.csv for an example.

from preprocessing import clean_smiles_batch

#Import your dataset
df = pd.read_csv("./data/test.csv") #your file directory 
apis = df["SMILES1"].values.tolist()
coformers = df["SMILES2"].values.tolist()

# Clean and canonicalize SMILES of API
apis = clean_smiles_batch(
    apis,
    uncharge=True,
    remove_stereochemistry=True,
    to_canonical=True,
)
# Clean and canonicalize SMILES of coformers 
coformers = clean_smiles_batch(
    coformers,
    uncharge=True,
    remove_stereochemistry=True,
    to_canonical=True,
)

Afterward, you can use the code under the examples folder to train your model! 🚀

SMILES Randomization 🔀

Starting from canonical SMILES, you can perform SMILES randomization on your data, using the code provided here. We observed SMILES randomization to strengthen the generalizability of the models and help estimate the prediction uncertainty.

Prediction 🔮

Now that you have your model (or you can use the DeepCocrystal model available here), you can predict the co-crystallization of any API-coformer pair model. How? Like this:

from deepcocrystal import smiles_preprocessing

# load the model
loaded_model = tf.keras.models.load_model("deepcocrystal-trained")

# load the data
test_api = smiles_preprocessing.space_separate_smiles_list(apis)
test_coformer = smiles_preprocessing.space_separate_smiles_list(coformers)

# predict
predictions = loaded_model.predict(
    x=[test_api, test_coformer], 
    batch_size = 512,
    verbose = 1
    )

Voila! You have your co-crystallization predictions 🎉

Important

The deepcocrystal-trained model provided here is trained only on non-proprietary data to allow a academic license (560 less API-coformer pairs). The accuracy of this model is 75% and the sensitivity is 70%.

Closing Remarks 🎆

Thanks again for finding our code interesting! Please consider starring the repository ✨ and citing our work if this codebase has been useful for driving your experimental tests 👩‍🔬 👨‍🔬

@article{birolo2024deep,
  title={Deep Supramolecular Language Processing for Co-crystal Prediction},
  author={Birolo, Rebecca and {\"O}z{\c{c}}elik, R{\i}za and Aramini, Andrea and Gobetto, Roberto and Chierotti, Michele Remo and Grisoni, Francesca},
  year={2024}
}

If you have any questions, please don't hesitate to open an issue in this repository. We'll be happy to help 🕺

Hope to see you around! 👋 👋 👋