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arXiv · 2507.08162

AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration

Peng Qiu·Hanqi Feng·Meng-Chun Zhang·Barnabas Poczos
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Abstract

Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "non-toxic." AmpLyze closes this gap by predicting the actual HC50 value from 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% 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.

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Peng Qiu, Hanqi Feng, Meng-Chun Zhang, Barnabas Poczos. 2025-07-10. AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration. https://arxiv.org/abs/2507.08162

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