MetaMDA: explainable prediction of microbe-drug association utilizing random walks on a microbe-metabolite-drug heterogeneous network.

Abstract

Human-associated microbes play a critical role in physiological processes and disease development, including cancer. Predicting microbe-drug associations (MDAs) can aid drug discovery and personalized medicine. However, existing methods cannot predict MDAs involving microbes or drugs absent from labeled data, and they fail to model the underlying biological mechanisms between microbes and drugs. To address these limitations, we propose a novel computational framework, named MetaMDA, for predicting MDAs by performing random walks on a microbe-metabolite-drug heterogeneous network. MetaMDA first constructs a heterogeneous graph that integrates microbes, metabolites, and drugs, enabling the modeling of complex biological interactions. A random walk algorithm with tailored transition probabilities is subsequently applied to the graph, effectively capturing features from multiple node types on a unified scale. Experimental results across multiple datasets demonstrate that MetaMDA consistently outperforms state-of-the-art methods, achieving an average improvement of 26%. Notably, we show MetaMDA's unique ability to predict MDAs involving microbes or drugs absent from labeled data, as illustrated by associations related to acarbose. Furthermore, mechanistic analysis of MetaMDA provides biological explanations for the associations between Escherichia coli and escitalopram, highlighting its potential to reveal a deeper mechanistic understanding of microbe-drug associations. The code and datasets are available on Zenodo https://doi.org/10.5281/zenodo.17348446 and GitHub https://github.com/wqlyt17/MetaMDA.