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Peptide-based Supramolecular Co-assembly Design: Multiscale Machine Learning Modeling Approach

CORDIS · observation · Publication date unknown

Peptide-based Supramolecular Co-assembly Design: Multiscale Machine Learning Modeling Approach Supramolecular self-assembly is a fundamental process abundantly utilized by nature and emerging functional materials technologies ranging from drug delivery to soft semiconductor devices. Recently, an increased focus has been placed on the multicomponent peptide co-assembly as they often display unique emergent properties that can dramatically expand the functional utility of peptide-based materials. Still, the full potential is hindered by the combinatorial complexity of peptide-based materials and our inability to predict the co-assembled structures and, therefore, properties and functionality. Machine Learning models built on top of Molecular Dynamics simulations are ideally suited to decipher the co-assembly behavior. However, the existing molecular models either suffer from severe approximations disabling them to give accurate predictions or are c

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recordType

award

status

SIGNED

region

EU

value

1474182

unit

EUR