Next-Gen NMR Prediction and Analysis: A Cutting-Edge Tool for High-Precision Structure Identification

Description du projet

Logiciel de RMN avancé pour la médecine et le diagnostic personnalisés

La résonance magnétique nucléaire (RMN) est une technique analytique puissante largement utilisée en chimie, en biochimie et en biologie moléculaire pour étudier la structure moléculaire, les changements de conformation et les interactions avec des biomolécules complexes telles que les protéines et les acides nucléiques. Le projet HQS-NextNMR, financé par le CEI, se propose d’étendre l’utilisation de la RMN à la médecine personnalisée en remédiant aux limitations actuelles de sensibilité et de résolution. Les chercheurs vont développer des logiciels avancés qui intègrent les calculs de chimie quantique aux données empiriques afin d’améliorer la précision. L’idée est d’accélérer l’analyse RMN et de faire progresser son utilisation dans des secteurs sensibles aux coûts tels que les produits pharmaceutiques, la biotechnologie et les sciences de l’environnement.

Objectif

The proposal aims to advance diagnostic technology by enhancing Nuclear Magnetic Resonance (NMR) as a tool for personalized medicine. Current NMR techniques face limitations in sensitivity, resolution, and uncertainty quantification, particularly in analyzing complex biological mixtures. These challenges hinder its potential to deliver the precise, non-invasive diagnostics required in personalized healthcare. Our solution is an advanced NMR software that integrates quantum-chemical calculations with empirical data to significantly improve NMR's accuracy and reliability.
Current methods fall short in accuracy when dealing with complex or novel molecular structures. Our tool addresses these limitations by providing accurate conformer sampling and error propagation techniques to quantify uncertainties in peak assignments and chemical shift variations, which are crucial for high-precision analysis. Additionally, it aims to enable low-field NMR devices as a viable option for advanced diagnostics, particularly in cost-sensitive sectors such as pharmaceuticals, biotechnology, and environmental science. The software automates many aspects of NMR analysis, reducing time and effort for researchers while improving accuracy, enabling wider adoption and faster results.
This innovation is particularly relevant to the EIC Transition program’s focus on bringing deep-tech innovations from lab to market. The software has the potential to revolutionize fields like drug discovery, personalized medicine, and environmental monitoring, opening new markets for point-of-care diagnostics and expanding the use of low-field NMR in industries previously restricted by high costs. Early interest from key players, including Merck and the University Medical Center Freiburg, supports the commercialization potential.