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Scripts for the research paper "SF-Rx: A Multi-output Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription"

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SF-RX

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.

Folder Structure

  • [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.

Data

  • 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.

Key Features

GPU Optimization

  • 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 in FEDERATED_LEARNING/experiment.py.

Dependencies

For any questions or issues, feel free to reach out to us via [shbae@drnoahbiotech.com], [dekim@drnoahbiotech.com], [jhyu@drnoahbiotech.com].

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Scripts for the research paper "SF-Rx: A Multi-output Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription"

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