AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration

In antimicrobial peptide development, red-blood-cell lysis ($\text{HC}_{50}$) is the principal safety barrier, but existing in silico tools stop at a binary toxicity classification. Here we propose a new method, AmpLyze, that closes this gap by predicting the actual $\text{HC}_{50}$ value from protein sequence alone and explaining the residues that drive toxicity. The model couples residue-level ProtT5/ESM2 embeddings with sequence-level descriptors in dual local and global branches, aligned by a cross-attention module and trained with log-cosh loss for robustness to assay noise. The optimal AmpLyze model reaches a PCC of 0.756 and an MSE of 0.987, outperforming classical regressors and the state-of-the-art. Ablations confirm that both branches are essential, and cross-attention adds a further $1 \% \text{PCC}$ and 3% MSE improvement. Expected-Gradients attributions reveal known toxicity hotspots and suggest safer substitutions. By turning hemolysis assessment into a quantitative, sequence-based, and interpretable prediction, AmpLyze facilitates AMP design and offers a practical tool for early-stage toxicity screening.