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

Información del proyecto

SupraModel

Identificador del acuerdo de subvención: 101077842

DOI

10.3030/101077842

Fecha de la firma de la CE 20 Febrero 2023

Fecha de inicio 1 Abril 2023

Fecha de finalización 31 Marzo 2028

Financiado con arreglo a

• European Research Council (ERC)

Coste total

€ 1 474 182,50

Aportación de la UE

€ 1 474 182,00

Coordinado por

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

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

Período documentado: 2023-04-01 hasta 2025-09-30

Resumen del contexto y de los objetivos generales del proyecto

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.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

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.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

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