HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations

Emerging research continues to reveal the fundamental contributions of microbial communities to maintaining human physiological balance and advancing drug discovery. However, established wet-lab investigation techniques require significant time and resources. Contemporary research efforts have predominantly concentrated on establishing robust computational architectures to predict microbe-drug associations. Our research establishes a neural network architecture that synthesizes heterogeneous biological relationships with attentional factorization machines (HAFMMDA) to predict undiscovered microbe-drug linkages. The initial step involves assembling a heterogeneous network architecture integrating three key components: microbe similarity networks, drug similarity networks, and established microbe-drug interaction networks. HAFMMDA utilizes HIN2vec to extract feature representations of microbe-drug pairs. Finally, it combines second-order feature interactions and attention mechanism to perform comprehensive prediction. Five-fold cross-validation results confirmed excellent predictive performance with an AUC score of 0.9805, demonstrating statistically significant improvements over five contemporary baseline approaches. These findings corroborate HAFMMDA's effectiveness in uncovering verified drug-microorganism associations while simultaneously predicting innovative therapeutic-microbe relationships.