paccmann_predictor

Drug interaction prediction with PaccMann.

paccmann_predictor is a package for drug interaction prediction, with examples of anticancer drug sensitivity prediction and drug target affinity prediction. Please see our papers:

• Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders (Molecular Pharmaceutics, 2019). This is the original paper on IC50 prediction using drug properties and tissue-specific cell line information (gene expression profiles). While the original code was written in tensorflow and is available here, this is the pytorch implementation of the best PaccMann architecture (multiscale convolutional encoder).

• Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2 (Machine Learning: Science and Technology, 2021). In there, we propose a slightly modified version to predict drug-target binding affinities based on protein sequences and SMILES

NOTE: PaccMann acronyms "Prediction of AntiCancer Compound sensitivity with Multi-modal Attention-based Neural Networks".

PaccMann for affinity prediction:

Requirements

• conda>=3.7

Installation

The library itself has few dependencies (see setup.py) with loose requirements. To run the example training script we provide environment files under examples/IC50/.

Create a conda environment:

conda env create -f examples/IC50/conda.yml

Activate the environment:

conda activate paccmann_predictor

Install in editable mode for development:

pip install -e .

Example usage

In the examples directory is a training script train_paccmann.py that makes use of paccmann_predictor.

(paccmann_predictor) $ python examples/IC50/train_paccmann.py -h
usage: train_paccmann.py [-h]
                         train_sensitivity_filepath test_sensitivity_filepath
                         gep_filepath smi_filepath gene_filepath
                         smiles_language_filepath model_path params_filepath
                         training_name

positional arguments:
  train_sensitivity_filepath
                        Path to the drug sensitivity (IC50) data.
  test_sensitivity_filepath
                        Path to the drug sensitivity (IC50) data.
  gep_filepath          Path to the gene expression profile data.
  smi_filepath          Path to the SMILES data.
  gene_filepath         Path to a pickle object containing list of genes.
  smiles_language_filepath
                        Path to a pickle object a SMILES language object.
  model_path            Directory where the model will be stored.
  params_filepath       Path to the parameter file.
  training_name         Name for the training.

optional arguments:
  -h, --help            show this help message and exit

params_filepath could point to examples/IC50/example_params.json, examples for other files can be downloaded from here.

References

If you use paccmann_predictor in your projects, please cite the following:

@article{manica2019paccmann,
  title={Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders},
  author={Manica, Matteo and Oskooei, Ali and Born, Jannis and Subramanian, Vigneshwari and S{\'a}ez-Rodr{\'\i}guez, Julio and Mart{\'\i}nez, Mar{\'\i}a Rodr{\'\i}guez},
  journal={Molecular pharmaceutics},
  volume={16},
  number={12},
  pages={4797--4806},
  year={2019},
  publisher={ACS Publications},
  doi = {10.1021/acs.molpharmaceut.9b00520},
  note = {PMID: 31618586}
}

@article{born2021datadriven,
  author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
  doi = {10.1088/2632-2153/abe808},
  issn = {2632-2153},
  journal = {Machine Learning: Science and Technology},
  number = {2},
  pages = {025024},
  title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
  url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
  volume = {2},
  year = {2021}
}