Cyclic Peptide Therapeutic Agents Discovery: Computational and Artificial Intelligence-Driven Strategies

Abstract

Cyclic peptides have emerged as promising modulators of protein–protein interactions due to their unique pharmacological properties and ability to target extensive flat binding interfaces. However, traditional strategies for developing cyclic peptides are often hindered by significant resource constraints. Recent advancements in computational techniques and artificial intelligence-driven methodologies have significantly enhanced the cyclic peptide drug discovery pipeline, while breakthroughs in automated synthesis platforms have accelerated experimental validation, presenting transformative potential for pharmaceutical innovation. In this review, we examine state-of-the-art computational and artificial intelligence-driven strategies that address challenges such as peptide flexibility, limited data availability, and complex conformational landscapes. We discuss how the integration of physics-based simulations with deep learning techniques is redefining the design and optimization of cyclic peptide therapeutics and propose future perspectives to advance the precision and efficiency of cyclic peptide drug development, ultimately offering innovative solutions to unmet medical needs.