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Spectral Interpretability for Molecular Potentials Learning with Graph Neural Networks

Project description

Graph neural networks with spectral interpretability for modelling molecular interaction potentials

Graph neural networks (GNNs) used to model molecular interaction potentials are invaluable for developing new drugs and materials. However, the models are often so complex that it can be challenging to interpret which molecular interactions are most critical. With the support of the Marie Skłodowska-Curie Actions programme, the SIMPL project aims to improve the interpretability and reliability of graph neural networks. To do so, it will design GNNs integrated with spectral graph theory and algebraic topology to streamline models and to identify the most important interactions. The framework will then be applied to study immune recognition processes, such as peptide-major histocompatibility complex binding. Outcomes of the project could ultimately lead to greater efficiency in immunotherapy and vaccine development.

Objective

The SIMPL project (Spectral Interpretability for Molecular Interaction PotentiaLs) is designed to enhance the interpretability and efficiency of Graph Neural Networks (GNNs) used to model Molecular Interaction Potentials (MIPs). GNNs have greatly advanced the prediction of molecular interactions, which is crucial for fields like drug discovery and material science. However, the complexity of these models often makes it challenging to identify which molecular interactions are most critical, limiting their practical application. SIMPL addresses this challenge by incorporating Spectral Graph Theory and Algebraic Topology into GNNs, allowing for clearer interpretation and streamlined models, focusing on the most relevant interactions without sacrificing accuracy. Compared to traditional gradient-based methods, which can be unstable and sensitive to specific molecular conformations, SIMPL offers a robust approach. Instead of a localized insights that could vary significantly depending on the particular state of the molecule or be subject to vanishing or exploding gradients problems (especially in complex models), SIMPL provides a global perspective by assessing the relevance of interactions across the entire learning task. This ensure that the identified interactions are consistently important and reliable. This approach in practice forces a multi-body expansion of the MIP leading to a comprehensive understanding of molecular systems.
While SIMPL has broad applications, it will be applied to the study of immune recognition processes, such as peptide-Major Histocompatibility Complex (MHC) binding. The framework’s ability to reliably identify critical molecular interactions could significantly enhance our understanding of these processes, leading to more informed approaches in immunotherapy and vaccine development.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

FREIE UNIVERSITAET BERLIN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 202 125,12
Address
KAISERSWERTHER STRASSE 16-18
14195 BERLIN
Germany

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Region
Berlin Berlin Berlin
Activity type
Higher or Secondary Education Establishments
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Total cost

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