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

Project Information

SupraModel

Grant agreement ID: 101077842

DOI

10.3030/101077842

EC signature date 20 February 2023

Start date 1 April 2023

End date 31 March 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 474 182,50

EU contribution

€ 1 474 182,00

Coordinated by

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

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

Reporting period: 2023-04-01 to 2025-09-30

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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