SyAGram: Dual-Component Framework for Anti-Gram-Negative AMP Classification and Generation

Gram-negative bacteria are a major driver of antimicrobial resistance, yet most computational AMP methods are trained for broad-spectrum performance and do not capture outer-membrane specific constraints. We present SyAGram, a dual-component framework specialized for anti-Gram-negative peptide discovery. SyAGram-Synth is a discrete diffusion generator (D3PM) guided by a task-adapted ESM-2 encoder via crossattention, providing stable and controllable sequence synthesis. SyAGram-Pred is a hybrid classifier that fuses BiLSTM-based sequence features with global physicochemical descriptors to reflect charge and amphipathicity patterns critical for Gramnegative activity. On curated datasets, SyAGram-Pred attains 89.12% accuracy, 91.53% AUC, and 90.06% F1. SyAGram-Synth yields a mean predicted activity score of 0.856, with 92% of sequences predicted active ($\geq 0.5$) and 62.3 % at high confidence ($\geq 0.9$). Generated peptides exhibit physicochemical profiles consistent with natural AMPs. SyAGram thus offers a practical, targeted pipeline for peptide design against Gramnegative pathogens; a web demo is available at https://synergyg ram-4lsud.ondigitalocean.app/.