CAMERA: Cross-Feature Aligned Model for Recognizing Top Antimicrobial Peptides

Antimicrobial peptides (AMPs) represent a mechanistically distinct therapeutic strategy to combat antimicrobial resistance, yet their discovery is hindered by sequence diversity, experimental noise in assays, and strain-specific activity. We present CAMERA, a cross-feature aligned ranking framework that fuses protein-language embeddings, predicted secondary structure and physicochemical descriptors via an information-maximising alignment loss. CAMERA employs a Mixture-of-Experts (MoE) router to dynamically activate taskadaptive subnetworks, enabling efficient multi-species generalization. To benchmark fine-grained antibacterial efficacy, we curate Bac11, an 11-species dataset with horizontally comparable minimum-inhibitory-concentration labels. On Bac11 and two public single-species datasets, CAMERA improves NDCG@5 by 36.66 percentage points and AUC by 3.57 percentage points over state-of-the-art baselines. Ablation studies demonstrate that cross-modal alignment and an auxiliary attribute regressor contribute substantial, complementary improvements. Our results indicate that multimodal learning-to-rank with conditional computation offers a scalable approach for strain-aware AMP discovery and can be extended to other peptide-driven therapeutic tasks. All code and public datasets used in this paper are available at https://github.com/hydrogen599/CAMERA.