数字农科院2.0

Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives

文献类型: 外文期刊

作者: Liu, Xu;Guan, Feifei;Luo, Huiying;Yao, Bin;Tian, Jian

作者机构:

关键词: Bioactive peptides;Antimicrobial peptides;Antioxidant peptides;Anti-inflammatory peptides;Multifunctional peptides;Artificial intelligence

期刊名称: ABIOTECH

ISSN: 2096-6326

年卷期: 2025 年 7 卷 1 期

页码:

收录情况: EI(2025版) ; ; CSCD(2025-2026年度) ; ; 农林核心(2024版) ; ; ESCI(2025版)

摘要: Bioactive peptides, defined as amino acid chains exhibiting diverse biological functions such as antimicrobial, antioxidant, and anti-inflammatory activities, are primarily generated through protein digestion methods including enzymatic hydrolysis, physical processing techniques and controlled microbial fermentation. Conventional discovery techniques that rely on multi-stage separation processes, such as enzymatic digestion, ultrafiltration, ion-exchange chromatography, gel filtration chromatography, and reverse-phase high-performance liquid chromatography (RP-HPLC) inherently demand substantial laboratory resources and extended timeframes. To address these limitations, artificial intelligence (AI)-driven approaches have emerged as transformative discovery platforms. These computational pipelines systematically execute six critical phases: comprehensive data acquisition and curation, advanced feature engineering utilizing physicochemical descriptors, machine learning model construction using algorithms, iterative model training incorporating hyperparameter optimization, rigorous validation against benchmark datasets, and high-throughput bioactive peptide prediction. This comprehensive review critically evaluates recent AI applications across four key bioactive peptide categories including antimicrobial peptides, antioxidant peptides, anti-inflammatory peptides, and multifunctional variants. Furthermore, it proposes integrated enhancement strategies such as classifying peptides via their functional mechanism or using database-independent modeling approaches. Additionally, based on AI methods, scenariospecific peptide customization and prediction of bioactivity in digested proteomes are anticipated to be achieved in the future.

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