Peptide-protein interactions in drug discovery

Abstract

Peptide-protein interactions are emerging as a major focus in drug discovery because they underpin many biological processes and provide a powerful means to modulate protein-protein interactions (PPIs), including targets long considered "undruggable." Peptide therapeutics occupy a unique middle ground between small molecules and biologics, combining high target selectivity with structural and functional versatility. This review integrates experimental and computational perspectives, summarizing current strategies for peptide ligand discovery, AI-based modeling, and generative peptide design. We highlight therapeutic applications, including peptide modulators of challenging targets such as Kirsten rat sarcoma viral oncogene homolog (KRAS), murine double minute 2 (MDM2), and programmed cell death ligand 1 (PD-L1), and discuss how deep learning is reshaping the peptide drug discovery paradigm. Looking ahead, integrating modern high-throughput screening technologies that generate large-scale interactome data with AI-driven models in a closed loop may further advance iterative peptide design and optimization. Collectively, these developments position peptide-protein interactions as an increasingly important component of next-generation precision therapeutics.