# A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification

arXiv: https://arxiv.org/abs/2511.04814 · HTML: https://mlsanity.com/paper/2511.04814/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 69 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Artificial intelligence-driven antimicrobial peptide discovery](https://mlsanity.com/paper/2308.10921/) (2023)

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

- [AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction](https://mlsanity.com/paper/2607.25518/) (2026)
- [Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling](https://mlsanity.com/paper/2608.30337/) (2026)

## Abstract (arXiv)

Antimicrobial peptides have emerged as promising molecules to combat antimicrobial resistance. However, fragmented datasets, inconsistent annotations, and the lack of standardized benchmarks hinder computational approaches and slow down the discovery of new candidates. To address these challenges, we present the Expanded Standardized Collection for Antimicrobial Peptide Evaluation (ESCAPE), an experimental framework integrating over 80.000 peptides from 27 validated repositories. Our dataset separates antimicrobial peptides from negative sequences and incorporates their functional annotations into a biologically coherent multilabel hierarchy, capturing activities across antibacterial, antifungal, antiviral, and antiparasitic classes. Building on ESCAPE, we propose a transformer-based model that leverages sequence and structural information to predict multiple functional activities of peptides. Our method achieves up to a 2.56% relative average improvement in mean Average Precision over the second-best method adapted for this task, establishing a new state-of-the-art multilabel peptide classification. ESCAPE provides a comprehensive and reproducible evaluation framework to advance AI-driven antimicrobial peptide research.