GCN-Mamba: Graph Convolutional Network with Mamba for Antibacterial Synergy Prediction

ABSTRACT The escalating crisis of antimicrobial resistance necessitates novel therapeutic strategies, among which drug combination therapy shows great promise by enhancing efficacy and reducing toxicity. However, identifying effective synergistic pairs from the vast combinatorial space remains experimentally challenging and resource-intensive. To address this, we introduce GCN-Mamba, a deep learning framework that integrates Graph Convolutional Networks (GCN) with the Mamba State Space Model. This architecture captures both local molecular topological structures and global implicit interactions by leveraging Extended 3-Dimensional Fingerprints (E3FP) and bacterial gene expression profiles. Evaluation on a comprehensive dataset demonstrated that GCN-Mamba significantly outperforms classical machine learning models in predictive accuracy. In a targeted case study against Methicillin-resistant Staphylococcus aureus (MRSA), the model successfully rediscovered known synergistic pairs, such as Quercetin and Curcumin, consistent with recent literature. Furthermore, prospective in vitro validation confirmed a novel synergistic combination of Shikimic acid and Oxacillin, validating the model’s practical utility. By efficiently prioritizing potential candidates, GCN-Mamba serves as a powerful and reliable tool for accelerating the discovery of synergistic antimicrobial combinations, effectively bridging the gap between computational prediction and experimental validation. Abbreviations - ECFP - Extended-Connectivity Fingerprints - E3FP - Extended 3-Dimensional Fingerprint - GNNs - Graph Neural Networks - SSM - State-Space Model - GCN - Graph Convolutional Networks - MLP - Multi Layer Perceptron - ABR - Antibiotic Resistance - TCM - Traditional Chinese Medicine - FLOPs - Floating-Point Operations - SMILES - Simplified Molecular Input Line Entry System - DGL - Deep Graph Library - FICI - Fractional Inhibitory Concentration Index - CI - Combination Index - CNNs - Convolutional Neural Networks - SVM - Support Vector Machine - ACC - Accuracy - MCC - Matthews Correlation Coefficient - TPR - True Positive Rate - LODO-CV - Leave-one-drug-out cross-validation - LOSO-CV - Leave-One-Strain-Out cross-validation