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AttentionDTA_BIBM

AttentionDTA: prediction of drug–target binding affinity using attention model.https://ieeexplore.ieee.org/abstract/document/8983125

This repository contains the source code and the data.

AttentionDTA

Setup and dependencies

Dependencies:

  • python 3.6
  • tensorflow >=1.9
  • numpy

Resources:

  • README.md: this file.
  • tfrecord: The original data set and data set processing code are saved in this folder.
    • davis_div.txt: Under the 5-fold cross-validation setting, there is a division of the training set and the test set of the davis dataset.
    • kiba_div.txt: Under the 5-fold cross-validation setting, there is a division of the training set and the test set of the kiba dataset.
    • davis_str_all.txt
    • kiba_str_all.txt
    • dataset.py: create data in tfrecord format according to (kiba/davis)_div.txt
  • DTA_train.py: train a AttentionDTA model.
  • DTA_model.py: AttentionDTA model architecture
  • DTA_test.py: test trained models

Step-by-step running:

1. Create data in tfrecord format

python dataset.py

2. Train a prediction model

python DTA_train.py To train a model using training data.

3. Predict affinity with trained models

python DTA_test.py