/AttentionDTA_TCBB

AttentionDTA: drug--target binding affinity prediction by sequence-based deep learning with attention mechanism

Primary LanguagePython

AttentionDTA_TCBB

AttentionDTA: drug--target binding affinity prediction by sequence-based deep learning with attention mechanism

This repository contains the source code and the data.

Setup and dependencies

Dependencies:

  • python 3.6
  • pytorch >=1.2
  • numpy
  • sklearn
  • tqdm
  • tensorboardX
  • prefetch_generator

Resources:

  • README.md: this file.

  • datasets: The datasets used in paper.

    • KIBA.txt:
    • Metz.txt:
    • Davis.txt

    In the directory of data, we now have the original data "./datasets/KIBA.txt" as follows:

     Drug_ID       Protein_ID   Drug_SMILES          Amino_acid_sequence     affinity
 CHEMBL1087421 O00141       COC1=C(C=C2C(=C...   MTVKTEAAKGTLTYSRMRGM... 11.1

  • dataset.py: data process.

  • AttentionDTA_main.py: train and test the model.

  • Hyperparameter_research.py: hyperparameter seach of AttentionDTA

  • model.py: AttentionDTA model architecture

  • Learning_rate_select.py: find the suitable learning rate

Run:

python HpyerAttentionDTI_main.py

python Learning_rate_select.py

python Hyperparameter_research.py