Peptides occupy a unique niche as biochemical tools: they are small, modular reagents capable of perturbing protein function with a precision that is often inaccessible to small molecules or antibodies. Historically, their broader use in biochemical research has been constrained by slow discovery workflows, limited control over specificity, and poor physicochemical properties. Recent advances in artificial intelligence have begun to change this landscape by enabling the rational, data-driven design of peptides tailored for specific experimental tasks. In this review, we focus on AI-designed peptides as practical tools for biochemistry. We survey sequence-based and structure-based design paradigms, highlighting how each supports distinct classes of peptide tools, including isoform- and motif-specific binders, multi-objective assay-ready reagents, and functional peptides that enable degradation, stabilization, or biophysical interrogation of proteins. By emphasizing experimental utility, design constraints, and appropriate use cases, we aim to provide a framework for selecting and deploying AI-designed peptides as on-demand reagents in modern biochemical research.
Hong et al. (Fri,) studied this question.