PCMol

A multi-target model for de novo molecule generation. By using the internal protein representations of the AlphaFold^[1] model, a single SMILES-based transformer can generate relevant molecules for thousands of protein targets (embeddings are available for 4,331 proteins).

The model was trained using bioactivity data from the Papyrus^[2] dataset (661,613 unique protein-ligand pairs in total, 6,249,253 after augmentation).

AlphaFold Embedding dataset: Zenodo

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Molecules generated by the PCMol generative model when using AlphaFold latent embeddings of particular proteins (A-D) as input.

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Installation

1. Setup script (recommended)

The setup script will install the required dependencies and download the pretrained model.

git clone https://github.com/CDDLeiden/pcmol.git && cd pcmol
chmod +x setup.sh
bash setup.sh

2. Conda (alternative)

The conda route requires the user to download the pretrained model manually (link below).

# Setting up a fresh conda environment
git clone https://github.com/CDDLeiden/pcmol.git && cd pcmol
conda env create -f environment.yml && conda activate pcmol
python -m pip install -e .

*When not using the setup script, the pretrained model can be downloaded from here (mirror). It should then be placed in the .../pcmol/data/models folder.

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Generating molecules for a particular target

1. Using a script (conda route)

# Run the model on a single target using Accession ID (generates 10 SMILES strings)
conda activate pcmol
python pcmol/generate.py --target P29275

# If GPU is not available
python pcmol/generate.py --target P29275 --device cpu

If available, the appropriate AlphaFold2 embeddings to be used as input to the model will be downloaded automatically. The generated molecules are saved in the data/results folder.

2. Calling the generator directly

To generate molecules for a particular target, the Runner class can be used directly. The generate_smiles method returns a list of SMILES strings for a target protein specified by its Accession ID.

from pcmol import Runner

model = Runner(model="XL")
SMILES = model.targetted_generation(protein_id="P29275", batch_size=1, repeat=10)

Examples of molecules generated for different targets:

A single pretrained PCMol model can generate molecules for thousands of different targets.

More examples can be found in notebooks/generate.ipynb

List of supported protein targets

The model currently depends on the availability of AlphaFold2 embeddings for the target protein. The list of supported targets can be found in the data/targets.txt file.

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Paper & Authors

The paper is available on ACS Journal of Chemical Information and Modeling.

Citation (BibTeX)

@article{doi:10.1021/acs.jcim.4c00309,
author = {Bernatavicius, Andrius and Šícho, Martin and Janssen, Antonius P. A. and Hassen, Alan Kai and Preuss, Mike and van Westen, Gerard J. P.},
title = {AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models},
journal = {Journal of Chemical Information and Modeling},
volume = {0},
number = {0},
pages = {null},
year = {0},
doi = {10.1021/acs.jcim.4c00309},
    note ={PMID: 39475544},
URL = {https://doi.org/10.1021/acs.jcim.4c00309},
eprint = {https://doi.org/10.1021/acs.jcim.4c00309}
}

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References

[1]: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.

[2]: Béquignon, O. J., Bongers, B. J., Jespers, W., IJzerman, A. P., van der Water, B., & van Westen, G. J. (2023). Papyrus: a large-scale curated dataset aimed at bioactivity predictions. Journal of cheminformatics, 15(1), 3.