构建首个标准化多标签抗菌肽数据集，提升药物发现效率
A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification
- 整合8万+肽段，建立生物一致的多标签分类体系
- 新模型在平均精确率上比次优方法提升2.56%
- 适合从事抗生素替代品研发的生物信息学研究者
抗菌肽是应对耐药性的潜在分子。但数据分散、标注不一及缺乏标准基准制约了计算方法发展。为此，我们提出扩展标准化抗菌肽评估集合（ESCAPE），整合来自27个已验证库的超8万条肽段。该数据集将抗菌肽与非活性序列分离，并将其功能注释融入生物一致的多标签层级中，涵盖抗菌、抗真菌、抗病毒和抗寄生虫等多种活性。基于ESCAPE，我们提出一种融合序列与结构信息的Transformer模型，可预测肽的多重功能。该方法在平均精确率上相比次优方法实现最高2.56%的相对提升，确立了新的多标签肽分类性能标杆。ESCAPE为人工智能驱动的抗菌肽研究提供了全面且可复现的评估框架。
原文摘要 · Abstract (English)
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.
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