Computational/AI-aided Peptide Screening (also called in silico peptide screening) is a modern discovery workflow that uses physics-based simulation, statistical learning, and deep learning to search large peptide sequence spaces for candidates likely to meet a target function—such as binding a protein pocket, disrupting an interface, penetrating cells, or achieving a desired bioactivity—while simultaneously filtering for “developability” (solubility, stability, toxicity, immunogenicity risk, and manufacturability). The core advantage is leverage: instead of testing millions of peptides experimentally, teams can prioritize a small, high-quality shortlist by combining virtual screening, ML prediction, and iterative optimization loops. 1) What “Peptide Screening” Means in the AI + Computational Era A peptide screening problem usually has one (or more) of these goals: Function-first screening: find sequences predicted to perform a biological function (e.g., antimicrobial, signaling, inhibitory, cell-penetrating). Target-first screening: find peptides predicted to bind a defined target (enzyme active site, receptor pocket, protein–protein interface). Property-first screening: find peptides with favorable developability characteristics, then verify function. Historically, wet-lab screening approaches (e.g., library panning) dominate discovery. Computational/AI-aided peptide screening complements these by (a) generating/curating large virtual libraries and (b) ranking them using scoring functions and predictive models before committing to experiments. 2) Data Foundations: Where “Learning” Comes…
