Merck and Texas Tech Describe PepOpt, a Machine-Learning Tool for Optimizing Peptides Built With Unnatural Amino Acids
Researchers from Merck's discovery chemistry group and Texas Tech University reported in npj Drug Discovery on October 12, 2026 PepOpt, a machine-learning framework for peptide lead optimization that combines chemical-structure and sequence descriptions in simple linear models. The authors report that it predicts activity accurately, can handle noncanonical amino acids absent from its training data when their building blocks are represented, breaks each prediction into contributions from individual residues, and can search peptide design spaces of more than 10^50 possibilities without testing every sequence. The paper presents a design tool, not a new drug.