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

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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

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