Machine Learning-Based Screening Tool for Discovery and Design of Membrane-Active Peptides

Dr. Ferguson, in collaboration with colleagues from UCLA, has invented a machine-learning based tool to screen peptides for membrane activity. The tool itself consists of a proprietary computational platform that leverages machine learning on large, experimentally validated datasets to discover and design membrane-active ⍺–helical proteins and peptides.  This results in a self-consistent screening tool that can differentiate between membrane-permeating and non-membrane-permeating helical peptides.