Nazmul Shuzan

Machine-Learning-Guided Design of Antifreezing Peptides

An unsupervised machine-learning framework that leverages hybrid high-dimensional peptide representations to discover high-performance AFPT families without requiring 3D structures or large labeled data sets is presented and demonstrates how unsupervised hybrid-feature learning can reveal actionable biophysical design rules from sequence data alone.