Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Abstract

Proposed de novo peptide design strategy against amyloidogenic targets. After initial computational preparation of the binder and target, the computational and experimental validation are incorporated in iterative machine learning powered cycles to generate better and improved peptide-based targets.