# DHCLHAM: microbe-drug interaction prediction based on dual-hypergraph contrastive learning framework with hierarchical attention mechanism

## TL;DR

This paper introduces a new AI-based method to predict how drugs interact with gut microbes, improving personalized medicine.

## Contribution

A novel dual-hypergraph contrastive learning framework with hierarchical attention for microbe-drug interaction prediction.

## Key findings

- DHCLHAM outperforms existing models on benchmark datasets with high AUC and AUPR scores.
- The model achieved 98.61% AUC and 98.33% AUPR on the aBiofilm dataset.
- The framework integrates AI and network pharmacology for analyzing microbe-drug interactions.

## Abstract

Various drugs can markedly disrupt gut microbiota, resulting in a reduction of beneficial microbial populations and precipitating a range of negative clinical consequences. Traditional experimental methods have considerable limitations in clarifying the mechanisms of microbe-drug interactions, thereby necessitating the creation of innovative computational techniques to establish theoretical foundations for personalized and precision medicine. However, the majority of current computational methods rely on graph structures, which inadequately represent the intricate, varied, and heterogeneous interactions among multiple drugs and microbial communities.

We introduce a hierarchical attention-driven dual-hypergraph contrastive learning framework for predicting microbe-drug interactions. Initially, the original bipartite graph and various similarity data are integrated using nonlinear features by incorporating the functional similarity of medicinal chemical attributes and microbial genomes, alongside computing the Gaussian kernel similarity. Subsequently, a dual network structure comprising K-Nearest Neighbors (KNN) hypergraph and K-means Optimizer (KO) hypergraph is established, employing a hierarchical attention mechanism to facilitate collaborative information aggregation between hyperedges and hypernodes. A contrastive learning approach is implemented to enhance the representation of the heterogeneous hypergraph space, and the prediction scores for microbe-drug interactions are derived by dynamically integrating two-channel embedded features via multi-head attention.

Experiments conducted on various publicly accessible benchmark datasets demonstrate that the DHCLHAM model markedly surpasses the current optimal model in critical metrics, including AUC and AUPR. Particularly on the aBiofilm dataset, the AUC and AUPR attained 98.61% and 98.33%, respectively.

A computational framework was developed through multi-dimensional case validation, integrating artificial intelligence and network pharmacology principles, offering a novel paradigm for analyzing microbe-drug interaction mechanisms. The research findings hold significant reference value for optimizing clinical treatment protocols and establish a theoretical foundation to develop precise medication strategies aimed at intestinal flora.