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

Projektinformationen

SupraModel

ID Finanzhilfevereinbarung: 101077842

DOI

10.3030/101077842

EK-Unterschriftsdatum 20 Februar 2023

Startdatum 1 April 2023

Enddatum 31 März 2028

Finanziert unter

• European Research Council (ERC)

Gesamtkosten

€ 1 474 182,50

EU-Beitrag

€ 1 474 182,00

Koordiniert durch

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

Periodic Reporting for period 1 - SupraModel (Peptide-based Supramolecular Co-assembly Design: Multiscale Machine Learning Modeling Approach)

Berichtszeitraum: 2023-04-01 bis 2025-09-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Peptides, short chains of amino acids, can spontaneously assemble into multi-functional materials used in emerging technologies ranging from drug delivery to soft semiconductor devices. Designing these materials is challenging because scientists must identify the best amino acid sequence among enormous possibilities, much larger than one can experimentally test. Thus, there is an urgent need to understand the underlying principles that will allow for a shift from a human intuition-based to a rational design-based approach. The EU funded SupraModel project aims to develop an efficient and rapid framework for predicting peptide co-assembly structures while providing unprecedented molecular insight into the co-assembly process.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

Novel strategies were developed to enhance the accuracy and efficiency of Graph Neural Network-based molecular models. These methods were implemented in new, well-documented software tools, reducing the entry barrier to the state-of-the-art simulation techniques for a general molecular modeling community.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Accurate and efficient molecular models of organic molecules are essential to advancing many scientific fields, ranging from medicine to environmental sciences.