Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations

Overview

This repository contains Python codes and datasets necessary to run the MolecBioNet model. MolecBioNet is a novel graph-based framework that integrates molecular and biomedical knowledge for robust and interpretable DDI prediction. By modeling drug pairs as unified entities, MolecBioNet captures both macro-level biological interactions and micro-level molecular influences, offering a comprehensive perspective on DDIs. The framework extracts local subgraphs from biomedical knowledge graphs and constructs hierarchical interaction graphs from molecular representations, leveraging classical graph neural network methods to learn multi-scale representations of drug pairs. To enhance accuracy and interpretability, MolecBioNet introduces two domain-specific pooling strategies: context-aware subgraph pooling (CASPool), which emphasizes biologically relevant entities, and attention-guided influence pooling (AGIPool), which prioritizes influential molecular substructures. Please take a look at our paper for more details on the method.

Install the Environment

OS Requirements

The package development version is tested on Linux (Ubuntu 20.04) operating systems with CUDA 12.1.

Python Dependencies

MRHGNN is tested under Python == 3.10.14.

We provide a txt file containing the necessary packages for MolecBioNet. All the required basic packages can be installed using the following command:

pip install -r requirements.txt

Usage

To train and evaluate the model, you could run the following command.

• Ryu's Dataset

python train.py --dataset 'Ryu' --batch_size 512 --eval_every_iter 225 --alpha 2 --beta 10 --lr 1e-3 --weight_decay_rate 1e-5 --num_epochs 80

• DrugBank Dataset

python train.py --dataset 'DrugBank'  --batch_size 512 --eval_every_iter 489 --alpha 2 --beta 10 --lr 1e-3 --weight_decay_rate 1e-5 --num_epochs 80

Supplementary Material

The supplementary material includes related work, detailed descriptions of the datasets, baseline configurations, novel drug experimental setups, ablation study designs, and evaluation metrics.
You can access the full document here: Supplementary_Information_MolecBioNet.pdf

Citation

Please kindly cite this paper if you find it useful for your research. Thanks!