AI-driven discovery of multifunctional peptides: From sequence space to therapeutics.
TL;DR
This review systematically examines the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings, and summarizes the practical applications of these models in peptide database mining, functional mechanism interpretation, and mutation effect prediction.
Abstract
Multifunctional peptides (MFPs), defined as single peptide sequences endowed with two or more biological activities, have emerged as a highly promising class of therapeutic candidates for the intervention of complex and multifactorial diseases. Nevertheless, their discovery has long been constrained by the intrinsic limitations of conventional high-throughput experimental screening and heuristic, rule-based computational strategies, both of which are labor-intensive, costly, low-yield, and poorly suited to the vast combinatorial landscape of peptide sequence space. Recent advances in artificial intelligence (AI), particularly deep learning and protein language models (ProtLMs), have fundamentally transformed this landscape by enabling data-driven, scalable, and representation-rich frameworks for MFP identification. These approaches have substantially improved the ability to capture contextual, structural, physicochemical, and evolutionary determinants of peptide multifunctionality, thereby opening new avenues for systematic peptide discovery. Given the rapid and fragmented progress across this field, a review is imperative to provide an integrated understanding of the methodological landscape, practical applications and persistent challenges in AI-driven MFP discovery. In this review, we first systematically examine the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings. We then summarize the practical applications of these models in peptide database mining, functional mechanism interpretation, and mutation effect prediction. Finally, we outline critical challenges and promising directions that remain in class imbalance, incomplete annotation, limited cross-dataset generalizability, and insufficient integration of drug-likeness and developability criteria.