GOA-PGN: Global Orientation-Aligned Physics Graph Network for Quantitative Antimicrobial Peptide Discovery

The rise of antibiotic-resistant bacteria drives urgent interest in antimicrobial peptides (AMPs). However, current computational discovery methods are hindered by single-modality representations that fail to capture the complex spatial mechanisms of membrane disruption. In this work, we propose a global orientation-aligned physics graph network (GOA-PGN) for predicting the minimum inhibitory concentration (MIC) of AMPs. GOA-PGN operates through two parallel paths to integrate localized sequence motifs with structural biophysical properties. One path leverages a peptide-tuned language model and partial convolutions to extract fine-grained sequential patterns. Simultaneously, the other path is used to construct coordinate-free topological graphs on the basis of the cosine similarity of side chain orientations to bypass static conformational noise. We integrate these dual representations via an attention mechanism to comprehensively capture global spatial amphipathicity, which is crucial for membrane disruption. Furthermore, the network incorporates a deep imbalanced regression (DIR) paradigm to mitigate the extreme label skewness prevalent in real-world bioactivity data. This paradigm uses dynamically weighted multitask loss to jointly predict the MIC and auxiliary physicochemical properties and employs a two-stage optimization strategy of adapter warm-up and subsequent joint fine-tuning to explicitly prioritize highly potent candidates. When evaluated on the DBAASP and CAMP datasets under the sequence homology constraints established by the quantitative mapping of antibacterial peptide (QMAP) benchmark, GOA-PGN demonstrates robust predictive accuracy across multiple bacterial strains. The code is publicly available at https://anonymous.4open.science/r/AMP-F316.