Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network
DrugVNN is an end-to-end drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN), and learns drug representation through a node-edge communica-tive message passing network (CMPNN). Between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs.
We provide linux commands to install the environment. You will need the conda package manager, which can be installed from here.
To install the required packages, follow there instructions (tested on a linux terminal):
-
clone the repository
git clone https://github.com/biomed-AI/DrugVNN -
cd into the cloned directory
cd DrugVNN -
install the environment using anaconda3
conda env create -f environment.yaml
Simply run:
python main.py --mode test --model_path ./results/DrugVNN.pth --only_combine_child_gene_group
data and trained model can be found in here