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

Informacje na temat projektu

SupraModel

Identyfikator umowy o grant: 101077842

DOI

10.3030/101077842

Data podpisania przez KE 20 Lutego 2023

Data rozpoczęcia 1 Kwietnia 2023

Data zakończenia 31 Marca 2028

Finansowanie w ramach

• European Research Council (ERC)

Koszt całkowity

€ 1 474 182,50

Wkład UE

€ 1 474 182,00

Koordynowany przez

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

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

Okres sprawozdawczy: 2023-04-01 do 2025-09-30

Podsumowanie kontekstu i ogólnych celów projektu

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.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

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.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

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