PharML is a framework for predicting interaction between protein structures and ligands. It utilizes a novel Molecular-Highway Graph Neural Network (MH-GNN) architecture based on state-of-the-art techniques in deep learning. This repository contains visualization, preprocessing, training, and inference code written in Python and C. In addition, it provides an ensemble of pre-trained models which can be used for generating predictions of compound binding relative to a given target.
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Edit and execute the create_env_ubuntu.sh script considering your system configuration
emacs tools/create_env_ubuntu.sh cd tools ; bash create_env_ubuntu.sh
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Preprocess the Dataset(s)
-> cd datasets/covid19 ; make
-> After preprocessing completes, you will have a directory containing -> data/lig: Ligand graph files -> data/nhg: Protein neighborhood graph files -> data/pdb: Raw pdb files used to generate ligands / NHGs -> data/map: Map file used for inference, ligand-to-target tests
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Test Inference Across example map file
-> cd pharml.bind/ ; bash inference.sh