A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification

TL;DR

This paper introduces ESCAPE, a comprehensive benchmark dataset for multilabel antimicrobial peptide classification, and a transformer-based model that achieves state-of-the-art performance, facilitating AI-driven discovery of new antimicrobial agents.

Contribution

The paper presents ESCAPE, a large standardized dataset with functional annotations, and a novel transformer-based model for multilabel peptide activity prediction, advancing computational antimicrobial research.

Findings

ESCAPE dataset includes over 80,000 peptides with multilabel annotations.

The proposed model achieves up to 2.56% improvement in mean Average Precision.

State-of-the-art performance established for multilabel antimicrobial peptide classification.

Abstract

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.