Predicting protein-ligand binding affinities using Convolutional Neural Networks (CNN)

This is an implementation of the CNN network architecture described in the following paper

KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
José Jiménez , Miha Škalič , Gerard Martínez-Rosell , and Gianni De Fabritiis
DOI: 10.1021/acs.jcim.7b00650

Requirements

• Tensorflow : pip install tensorflow==1.14
• Keras: pip install keras==2.2.4
• Scikit-learn: pip install -U scikit-learn
• oddt: conda install -c oddt oddt
• molekulekit: pip install moleculekit
• copy the dataset/refined-set-2016 folder into the dataset folder(this repository) from https://github.com/hassanmohsin/DLSCORE-CNN

Training the model (by generating augmented data on the fly)

• First extract the voxel features using the script Featurizer.ipynb insdie the directory training_with_htmd. The script will create the file called data.h5 inside the dataset dir
• Run the script trainer.ipynb to train the model.