Awesome

Code for the paper: "PIGNET: A physics-informed deep learning model toward generalized drug-target interaction predictions" by Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim

Table of Contents

• Install Dependencies
• Running
  • Data Preprocessing
  • Train
  • Model benchmark with CASF2016 benchmark and csar
• Command Examples
• Citing this work

Install Dependencies

PIGNet needs conda environment. After installing conda,

you can manually install the necessary packages for executing our code. The package list is shown as follows

• rdkit
• pytorch
• numpy
• biopython
• scikit-learn
• scipy
• smina

or you can just execute the followings in the command line.

./dependencies
conda activate pignet

Running

Data

You can generate the data.

• Training data by executing data/download_train_data.sh.
• Benchmark data by executing data/download_test_data.sh.

Train

You should download and process the data with data/download_test_data.sh.
Then, Trainig can be done with run.sh.
This process will use the data in ./data/pdbbind_v2019/*.

Model benchmark with CASF2016 benchmark and csar

You should download and process the data with data/download_test_data.sh.
After that, you can use the execution commands for the benchmark are shown in benchmarks/*/test.sh.

Citing this work

@article{moon2022pignet,
  title={PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions},
  author={Moon, Seokhyun and Zhung, Wonho and Yang, Soojung and Lim, Jaechang and Kim, Woo Youn},
  journal={Chemical Science},
  year={2022},
  publisher={Royal Society of Chemistry}
}