This code gives the tensorflow implementation of PaccMann as of our paper in Molecular Pharmaceutics. For new developments and pytorch implementations look at our PaccMann organization.
paccmann is a package for drug sensitivity prediction and is the core component of the repo.
The package provides a toolbox of learning models for IC50 prediction using drug's chemical properties and tissue-specific cell lines gene expression.
Please cite us as follows:
@article{oskooei2018paccmann,
title={PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks},
author={Oskooei, Ali and Born, Jannis and Manica, Matteo and Subramanian, Vigneshwari and S{\'a}ez-Rodr{\'\i}guez, Julio and Mart{\'\i}nez, Mar{\'\i}a Rodr{\'\i}guez},
journal={arXiv preprint arXiv:1811.06802},
year={2018}
}
@article{manica2019paccmann,
author = {Manica, Matteo and Oskooei, Ali and Born, Jannis and Subramanian, Vigneshwari and Saez-Rodriguez, Julio and Rodriguez Martinez, Maria},
title = {Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders},
journal = {Molecular Pharmaceutics},
year = {2019},
doi = {10.1021/acs.molpharmaceut.9b00520},
note = {PMID: 31618586},
}We strongly recommend to work inside a virtual environment (venv).
Create the environment:
python3 -m venv venvActivate it:
source venv/bin/activateThe module can be installed either in editable mode:
pip3 install -e .Or as a normal package:
pip3 install .Models can be trained using the script bin/training_paccmann that is installed together with the module. Check the examples for a quick start.
For more details see the help of the training command by typing training_paccmann -h:
usage: training_paccmann [-h] [-save_checkpoints_steps 300]
[-eval_throttle_secs 60] [-model_suffix]
[-train_steps 10000] [-batch_size 64]
[-learning_rate 0.001] [-dropout 0.5]
[-buffer_size 20000] [-number_of_threads 1]
[-prefetch_buffer_size 6]
train_filepath eval_filepath model_path
model_specification_fn_name params_filepath
feature_names
Run training of a `paccmann` model.
positional arguments:
train_filepath Path to train data.
eval_filepath Path to eval data.
model_path Path where the model is stored.
model_specification_fn_name
Model specification function. Pick one of the
following: ['dnn', 'rnn', 'scnn', 'sa', 'ca', 'mca'].
params_filepath Path to model params. Dictionary with parameters
defining the model.
feature_names Comma separated feature names. Select from the
following: ['smiles_character_tokens',
'smiles_atom_tokens', 'fingerprints_256',
'fingerprints_512', 'targets_10', 'targets_20',
'targets_50', 'selected_genes_10',
'selected_genes_20', 'cnv_min', 'cnv_max', 'disrupt',
'zigosity', 'ic50', 'ic50_labels'].
optional arguments:
-h, --help show this help message and exit
-save_checkpoints_steps 300, --save-checkpoints-steps 300
Steps before saving a checkpoint.
-eval_throttle_secs 60, --eval-throttle-secs 60
Throttle seconds between evaluations.
-model_suffix , --model-suffix
Suffix for the trained moedel.
-train_steps 10000, --train-steps 10000
Number of training steps.
-batch_size 64, --batch-size 64
Batch size.
-learning_rate 0.001, --learning-rate 0.001
Learning rate.
-dropout 0.5, --dropout 0.5
Dropout to be applied to set and dense layers.
-buffer_size 20000, --buffer-size 20000
Buffer size for data shuffling.
-number_of_threads 1, --number-of-threads 1
Number of threads to be used in data processing.
-prefetch_buffer_size 6, --prefetch-buffer-size 6
Prefetch buffer size to allow pipelining.