Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning
Daria De Raffele, Ioana M. Ilie
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.
Topics & Concepts
DruggabilityCyclic peptidePeptideComputational biologyComputer scienceArtificial intelligenceChemistryBiologyBiochemistryGeneChemical Synthesis and AnalysisClick Chemistry and ApplicationsMachine Learning in Bioinformatics