DOI: 10.5281/zenodo.17940975
title: Multi-Head Graph Attention with Residual Projection for drug–drug interaction (DDI) prediction

description: MULTI-HEAD GRAPH ATTENTION WITH RESIDUAL PROJECTION FOR DRUG–DRUG INTERACTION PREDICTION

A unified graph learning framework for accurate, robust, and interpretable modeling of pharmacological interactions through multi-head attention and projection-aligned residual reasoning



OVERVIEW

This repository provides an implementation of a graph-based deep learning framework for drug–drug interaction (DDI) prediction, integrating Multi-Head Graph Attention with a Residual Projection mechanism. The framework is designed to address fundamental challenges in pharmacological interaction modeling, including heterogeneous interaction semantics, deep graph over-smoothing, and limited generalization to unseen drug pairs.

Drugs are modeled as nodes in a pharmacological interaction graph, while edges represent diverse interaction types such as metabolic inhibition, synergistic enhancement, and transporter-mediated effects. By combining attention-based relational learning with projection-aligned residual connections, the framework enables stable deep reasoning, preserves semantic diversity across layers, and supports accurate prediction in sparse and noisy biomedical graphs.



KEY FEATURES

Multi-Head Graph Attention Module

The model employs multiple parallel attention heads to jointly learn diverse semantic representations of drug interactions. Each attention head focuses on a different relational perspective, enabling the model to capture complementary pharmacological mechanisms simultaneously.

This multi-head design improves expressiveness compared with single-head attention and conventional graph convolution approaches, and is particularly effective for heterogeneous and multi-relational interaction modeling.

Residual Projection Mechanism

To mitigate over-smoothing and feature collapse in deep graph neural networks, the framework introduces a residual projection mechanism. Input representations are projected into aligned latent subspaces before being fused with updated node embeddings.

This design preserves discriminative information across layers, stabilizes gradient propagation, and enables deeper graph architectures without performance degradation.

Relation-Aware Interaction Reasoning

The framework supports relation-aware modeling through relation-specific projections and scoring functions. It captures higher-order and compositional interaction dependencies, enabling inference over indirect and multi-hop interaction paths.

This capability is critical for cold-start scenarios involving previously unseen drug combinations.



DATASET PREPARATION

This repository is designed to interoperate with multiple open-access biomedical datasets commonly used in drug–drug interaction research.

Recommended Datasets




Dataset Name
Description
Source




Drug Interaction Graph Dataset
Large-scale benchmark containing annotated drug–drug interaction pairs with multiple interaction types
https://go.drugbank.com


Pharmaceutical Knowledge Network Dataset
Heterogeneous biomedical knowledge graph including drugs and side effects
http://sideeffects.embl.de


Molecular Attention Mapping Dataset
Provides molecular-level and atom-level supervision for attention interpretability
https://ctdbase.org


Residual Graph Reasoning Dataset
Benchmark for deep graph reasoning and multi-hop inference
https://ieee-dataport.org




Usage Note

Each dataset is distributed under its own licensing terms and usage policies. Users should review dataset documentation and properly cite original data providers when publishing derived results.



SYSTEM REQUIREMENTS




Component
Requirement




Python
Version 3.8 or higher


Deep Learning Framework
PyTorch 1.12 or higher


Graph Library
PyTorch Geometric


Scientific Computing
NumPy, scikit-learn


Molecular Processing
RDKit




GPU acceleration is recommended for large-scale experiments but is not strictly required.



ARCHITECTURE OVERVIEW

Model Components




Component
Function




Graph Attention Encoder
Aggregates neighborhood information using multi-head attention


Residual Projection Layer
Aligns and preserves feature subspaces across layers


Relation-Aware Scoring Module
Computes interaction scores based on relation semantics


Prediction Head
Outputs binary or multi-class DDI predictions




Information Flow

Drugs are encoded as graph nodes and passed through stacked graph attention layers. Residual projection layers align feature spaces between layers, ensuring semantic stability. Final embeddings are used to compute interaction scores for prediction.



EXPERIMENTAL RESULTS

The proposed framework has been evaluated on multiple benchmark datasets and compared with widely used baseline models.

Performance Comparison




Model
Accuracy
F1 Score
AUC




Graph Convolution Network
82.4%
0.801
0.845


Graph Attention Network
85.7%
0.834
0.872


GraphSAGE
84.9%
0.826
0.868


DeepDDI
86.3%
0.841
0.879


AutoDDI
87.1%
0.849
0.886


MHGAT with Residual Projection
90.8%
0.892
0.914




The results demonstrate that the proposed framework consistently outperforms baseline methods across all evaluation metrics.



APPLICATIONS




Application Area
Description




Computational Pharmacology
Prediction and analysis of adverse and synergistic drug interactions


Drug Discovery
Early-stage screening and prioritization of candidate drug combinations


Clinical Decision Support
Risk assessment for co-prescription scenarios


Biomedical Knowledge Graph Reasoning
Multi-relational link prediction and interaction inference






FUTURE WORK

Future research directions include incorporating temporal dynamics of drug interactions, developing scalable sparse attention mechanisms for very large graphs, integrating real-world clinical data such as electronic health records, and advancing explainability-driven pharmacological validation studies.



LICENSE

This project is released under the MIT License.



ACKNOWLEDGEMENTS

This work builds upon publicly available biomedical datasets and prior research in graph neural networks and drug–drug interaction prediction. We acknowledge the contributors of DrugBank, SIDER, CTD, and IEEE DataPort for providing open-access resources that made this research possible.