AI 中文总结
该研究推出PathoMIC基准数据集，建立小样本与零样本跨物种评估协议，开发知识增强框架，提升低资源物种MIC预测性能，为抗菌肽跨物种建模提供标准化基础。
AI 中文摘要
抗菌肽（AMPs）对多重耐药病原体具有活性，且作用机制与传统抗生素不同，是对抗抗生素耐药感染的有前景的方法。然而，其效力在不同病原体物种间差异很大，因此准确预测特定肽-病原体对的最低抑菌浓度（MIC），对于在成本高昂的实验验证前优先筛选候选物至关重要。现有预测器主要针对少数代表性良好的病原体进行训练，且很少利用跨物种的生物关系，限制了其对低资源和未见过的病原体的泛化能力。我们推出PathoMIC，这是最大、病原体多样性最丰富的用于定量抗菌肽活性预测的统一数据集，包含424种病原体物种的74751个实验报告的MIC测量值。PathoMIC整合了肽序列、标准化MIC值、病原体描述和分类关系，以促进相关物种间的知识迁移。我们建立了小样本和零样本跨物种评估协议，并开发了利用病原体描述和分类学的知识增强框架。该框架在监督有限的低资源物种上取得了显著改进，而对完全未见过的物种的收益仍然有限，凸显了零样本跨物种MIC预测的难度。PathoMIC为跨物种活性建模和病原体特异性虚拟筛选提供了标准化基础，代码可在该httpsURL获取。
英文摘要
With activity against multidrug-resistant pathogens and mechanisms distinct from conventional antibiotics, antimicrobial peptides (AMPs) offer a promising approach to combating antibiotic-resistant infections. However, their potency varies substantially across pathogen species, making accurate prediction of the minimum inhibitory concentration (MIC) for specific peptide-pathogen pairs essential for prioritizing candidates before costly experimental validation. Existing predictors are trained mainly on a few well-represented pathogens and rarely exploit biological relationships across species, limiting their generalization to low-resource and unseen pathogens. We introduce PathoMIC, the largest and most pathogen-diverse unified dataset for quantitative antimicrobial peptide activity prediction, containing 74,751 experimentally reported MIC measurements across 424 pathogen species. PathoMIC integrates peptide sequences, standardized MIC values, pathogen descriptions, and taxonomic relationships to facilitate knowledge transfer across related species. We establish few-shot and zero-shot cross-species evaluation protocols and develop a knowledge-enhanced framework that leverages pathogen descriptions and taxonomy. The framework yields substantial improvements for low-resource species with limited supervision, while gains for entirely unseen species remain modest, highlighting the difficulty of zero-shot cross-species MIC prediction. PathoMIC provides a standardized foundation for cross-species activity modeling and pathogen-specific virtual screening. Code is available at https://anonymous.4open.science/r/PathoMIC-546D/.
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