Next-Generation NMR Chemical Shift Predictions for Solvated Biomolecules and Functional Molecules

Project name in Czech
Next-Generation NMR Chemical Shift Predictions for Solvated Biomolecules and Functional Molecules
Annotation in Czech
Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is crucial for understanding molecular structure and interactions in solution. However, traditional implicit solvent models fail to capture key solute-solvent interactions, such as hydrogen bonding, leading to significant inaccuracies in predicted NMR parameters. To address this limitation, this project proposes a novel computational framework that integrates molecular dynamics (MD) simulations with machine learning (ML)-based NMR predictions. By explicitly modeling solvation effects, this approach aims to bridge the gap between experimental observations and computational predictions. The objectives of this research are threefold: (1) to develop an accurate computational model for NMR predictions in diverse solvents, (2) to extend the methodology to biomolecular systems and (3) to molecular photoswitches, particularly azobenzene derivatives. By enhancing the predictive power of NMR chemical shifts in solution, this study will provide critical insights into non-covalent interactions in complex molecular systems.

R&D category
ZV - Basic research
OECD FORD - main branch
10403 - Physical chemistry
OECD FORD - secondary branch
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OECD FORD - another secondary branch
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CEP - equivalent branches <br>(according to the <a href="http://www.vyzkum.cz/storage/att/E6EF7938F0E854BAE520AC119FB22E8D/Prevodnik_oboru_Frascati.pdf">converter</a>)
CF - Physical chemistry and theoretical chemistry

Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data delivery code
CEP26-GA0-GA-R
Data delivery date
Apr 29, 2026

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