3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs

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Article

https://doi.org/10.1039/D3RA00281K

Requirements

• Python >= 3.7
  
• PyTorch >= 1.9
  
• PyTorch Geometric >= 2.0
  

Usage

1. Activate a python environment with PyTorch and PyTorch Geometric
   
2. Clone the repository, navigate to the cloned folder
   git clone https://github.com/vtarasv/3d-prot-dta.git
cd 3d-prot-dta/
   
3. Install required packages
   pip install wheel
pip install -r requirements.txt
   
4. Run the experiments
   python test.py to obtain test datasets results as described in the manuscript
   python test.py --datasets davis to obtain only Davis test dataset results
   python test.py --datasets kiba to obtain only KIBA test dataset results
   The results will be saved in the results/ folder
   The log will be saved in the log/ folder
   
5. You can also launch the tuning process in the same way as described in the manuscript
   python tune.py --study my_study --sampler tpe
   The tuning results will be saved in the local storage sqlite:///dta_tune.db (in the same folder)
   

Data

See the corresponding README

Notes

• It is recommended to use GPU to speed up the experiments (machines with 1 GPU perform 20 times faster on average than machines with 4 CPUs)
• The training of 5 models (one per cross-validation train dataset) using one NVIDIA Tesla P100 SXM2 GPU takes about 10 and 40 hours for Davis and KIBA datasets respectively
• The code is tested on Ubuntu operating system