Skip to content

Deep-Learning-aided-Drug-Designing/DeepDTA

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

98 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

About DeepDTA: deep drug-target binding affinity prediction

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.

Figure

Installation

Data

Please see the readme for detailed explanation.

Requirements

You'll need to install following in order to run the codes.

You have to place "data" folder under "source" directory.

Usage

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}
}

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.2%
  • Shell 0.8%