ESCAPE: A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification

Expanded Standardized Collection for Antimicrobial Peptide Evaluation (ESCAPE) is an experimental framework for multilabel antimicrobial peptide classification. It combines a large-scale curated dataset, a benchmark for evaluating models, and a transformer-based baseline that integrates both sequence and structural information.

Abstract

ESCAPE introduces a standardized benchmark for multilabel antimicrobial peptide (AMP) classification. It unifies heterogeneous sources into consistent splits, label taxonomies, and metrics to enable rigorous comparison across models and training regimes. The benchmark includes tasks spanning AMP detection, activity-type multilabel classification (e.g., antibacterial, antifungal, antiviral, antiparasitic), and optional species-level target prediction. We release code, dataset definitions, and a public leaderboard to foster reproducible research in computational AMP discovery.

Overview Video

ESCAPE Database

The ESCAPE Dataset integrates over 80,000 peptide sequences from 27 validated public repositories to address critical limitations in existing AMP resources, including data fragmentation, inconsistent annotations, and limited functional coverage. It distinguishes antimicrobial peptides from negative sequences and organizes their functional annotations into a biologically meaningful multilabel hierarchy, covering antibacterial, antifungal, antiviral, and antiparasitic activities. The dataset comprises 21,409 experimentally validated AMPs and 60,950 non-AMPs filtered from unrelated sources.

ESCAPE graphical abstract

The ESCAPE Dataset is available for download. You can access the complete ESCAPE Database on Harvard Dataverse.

ESCAPE Benchmark

We evaluate six representative models for antimicrobial peptide classification: AMPlify, AMP BERT, TransImbAMP, amPEPpy, AMPs Net, and PEP Net, using the multilabel framework defined by ESCAPE. Each model was modified to support multilabel classification and trained with two fold cross validation. We report final performance by averaging predictions from both folds through an ensemble strategy. Evaluation uses two standard metrics for multilabel tasks: F1 score and mean Average Precision, which are suitable for datasets with class imbalance.

The table below summarizes the key methods for antimicrobial peptide classification of the ESCAPE Benchmark, their primary architectures, GitHub repositories, and the F1-score and mean Average Precision (mAP) these methods achieve by evaluating them on the ESCAPE Dataset.

Method	Primary Architecture	GitHub Repository	F1-score (%)	mAP (%)
Amps-Net	GCN	GitHub	57.7	54.2
TransImbAMP	Transformer-Based	GitHub	61.9	64.9
AMP-BERT	BERT	GitHub	66.1	66.2
amPEPpy	Random Forest (RF)	GitHub	65.0	68.0
PEP-Net	Transformer-Based	GitHub	65.2	68.2
AMPlify	Bi-LSTM with attention layers	GitHub	68.9	71.1
ESCAPE	Dual-branch transformer	GitHub	69.5	72.7

ESCAPE Baseline

The ESCAPE Baseline is a dual-branch transformer architecture designed to classify antimicrobial peptides (AMPs) using both sequence and structural information. It processes amino acid sequences through a transformer encoder and structural representations through a second branch that encodes peptide distance matrices. These two modalities are fused using a bidirectional cross-attention mechanism, enabling the model to capture both biological context and spatial structure. This approach achieves state-of-the-art overall performance on the ESCAPE Benchmark, outperforming existing methods in both F1-score and mean Average Precision.

ESCAPE baseline architecture

BibTeX

@article{escape2025,
  author  = {Sebastian Ojeda and Rafael Velasquez and Nicolás Aparicio and Juanita Puentes and Paula Cárdenas and Nicolás Andrade and Gabriel González and Sergio Rincón and Carolina Muñoz-Camargo and Pablo Arbeláez},
  title   = {ESCAPE: A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification},
  journal = {Preprint},
  year    = {2025},
  url     = {https://bcv-uniandes.github.io/escape-wp/}
}