AI-driven bioactive peptide discovery of next-generation metabolic biotherapeutics

Abstract

Metabolic diseases, including obesity and type 2 diabetes, pose a significant global health burden, demanding innovative therapeutic solutions. Traditional drug discovery is often slow and costly, struggling with the complex nature of these disorders. Bioactive peptides offer a promising alternative due characterized by their specificity, low toxicity, and diverse mechanisms. However, challenges in their screening, stability, and target identification have limited their clinical use. Artificial intelligence (AI) and machine learning (ML) are now revolutionizing peptide discovery. These technologies enable rapid prediction, de novo design, and optimization of bioactive sequences. This review critically evaluates AI's role in identifying and developing peptides for metabolic disease pathways. We examine key computational methods, including sequence-based features, advanced deep learning models (CNNs, LSTMs, Transformers), and generative approaches. The manuscript also covers essential datasets, validation frameworks, and illustrative case studies. We explore the integration of molecular dynamics, network pharmacology, and reinforcement learning for advanced peptide engineering. Despite significant progress, challenges persist, such as data heterogeneity, model generalizability, and the gap between in silico predictions and experimental validation. Looking ahead, we highlight future opportunities, including multi-omics integration, explainable AI, the discovery of microbiome-derived peptides, and synthetic biology-driven design. This review underscores AI’s transformative potential in advancing peptide-based interventions for metabolic diseases, offering a roadmap for novel, targeted, and preventive therapies.