In recent years, peptides have grabbed significant attention across many fields, including pharmaceutical, biomedical, and biotechnological industries, owing to their notable biological activity, low toxicity, and high specificity. Naturally occurring peptides play crucial roles in handling different biological processes (cellular signaling, immune responses, and enzymatic functions, etc.), while laboratory-made synthetic peptides can be adopted for many applied industrial applications. Following the practical problems in large-scale peptide synthesis in the laboratory which demand high cost and time, recent developments have benefited from the best use of machine learning (ML), deep learning, active learning, reinforcement learning (RL), generative artificial intelligence (AI), and large language models (LLMs) to reduce the number of experiments. ML algorithms enable the prediction of the peptide structure-activity relationship, bioavailability, and other drug-like properties with high accuracy. The integration of AI with peptide-based therapeutics design marks a paradigm shift in drug discovery which was otherwise dominated by small organic molecules. This review comprehensively examines AI-driven methodologies, including classical ML approaches, deep generative models, RL, and LLMs, that overcome historical limitations in peptide design, such as structural flexibility, enzymatic degradation, and membrane impermeability. Recent advances in structure-aware algorithms and sequence-based frameworks have accelerated peptide-based therapeutic development across oncology, metabolic disorders, and infectious diseases. Despite challenges in data scarcity and validation gaps, the convergence of computational prediction with experimental automation promises clinical translation of AI-designed peptides in the near future. This review highlights the transformative potential of AI in ushering a new era of precision peptide therapeutics.
Priyadarsinee et al. (Mon,) studied this question.