Machine-Learning Venom-Peptide Platform Builds 482-Scaffold Library and Hits All Four Test Targets
A venom-peptide discovery system published in Pharmaceuticals (MDPI) in 2026 paired phage display with a machine-learning model that predicts mutation-tolerant residues, building a library from roughly 482 venom-derived scaffolds. Screened against CD47, DLL3, IL33, and P2X7R, the resulting VCX library yielded strong binders for all four targets. Venom peptides are stabilized by multiple disulfide bonds and naturally evolved to hit GPCRs and ion channels, giving them structural stability that conventional peptides often lack.