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

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.