# AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration

arXiv: https://arxiv.org/abs/2507.08162 · HTML: https://mlsanity.com/paper/2507.08162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2007.06225/) (2020)
- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [Constructing sensible baselines for Integrated Gradients](https://mlsanity.com/paper/2412.13864/) (2024)

## Built on by (most cited first, 2 of 2)

- [PepBenchmark: A Standardized Benchmark for Peptide Machine Learning](https://mlsanity.com/paper/2604.10531/) (2026)
- [Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization](https://mlsanity.com/paper/2508.02834/) (2025)

## Abstract (arXiv)

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.