The approach used in this work is the modeling of protein sequences and compound 1D representations (SMILES) with convolutional neural networks (CNNs) to predict the binding affinity value of drug-target pairs.
Please see the README for detailed explanation.
You'll need to install following in order to run the codes. Refer to deepdta.yml for a conda environment tested in Linux.
- Python 3.4 <=
- Keras 2.x
- Tensorflow 1.x
- numpy
- matplotlib
- scikit-learn
You have to place "data" folder under "source" directory.
python run_experiments.py --num_windows 32 \
--seq_window_lengths 8 12 \
--smi_window_lengths 4 8 \
--batch_size 256 \
--num_epoch 100 \
--max_seq_len 1000 \
--max_smi_len 100 \
--dataset_path 'data/kiba/' \
--problem_type 1 \
--log_dir 'logs/'
For citation:
@article{ozturk2018deepdta,
title={DeepDTA: deep drug--target binding affinity prediction},
author={{\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan and Ozkirimli, Elif},
journal={Bioinformatics},
volume={34},
number={17},
pages={i821--i829},
year={2018},
publisher={Oxford University Press}
}