This repository contains the code used to reproduce the results from our research paper "SF-Rx: A Multi-output Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription". The software is organized into four folders, each corresponding to a specific task discussed in the paper. Below are detailed instructions and notes about the code structure and data.
- [SF_RX_MODEL]: Code and models for the SF-RX implementation, optimized for GPU environments.
- [GNNs]: Code for training GNNs and transformer models used in the paper.
- [FEDERATED_LEARNING]: Federated learning experiments with GPU parallelism.
- [PERMUTATION_TEST]: Permutation test for distributional shifts of scaffold structures.
- All required data is located in the
data
folder within each directory. - For large files, Google Drive links are provided in the respective folders.
- Note: The original results in the paper were generated using proprietary DrugBank data, which cannot be shared. Instead, we created toy datasets by combining publicly available data from DrugBank and PDR.
- SF-RX Model and Federated Learning tasks are designed to run on GPU environments.
- Federated Learning assumes 4 GPUs for parallel execution due to the computationally intensive nature of the FL experiments.
- To modify GPU settings, update the
parallelism
section inFEDERATED_LEARNING/experiment.py
.
- All software and library version requirements are listed in
dependencies.txt
.
For any questions or issues, feel free to reach out to us via [shbae@drnoahbiotech.com], [dekim@drnoahbiotech.com], [jhyu@drnoahbiotech.com].