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

Authors

Xu Liu, Feifei Guan, Huiying Luo, Bin Yao, Jian Tian

Published in

aBIOTECH. Volume 7. Issue 1. Pages 100014. Epub Dec 03, 2025.

Abstract

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, scenario-specific peptide customization and prediction of bioactivity in digested proteomes are anticipated to be achieved in the future.

PMID:
41940145
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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