Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents.

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Macrocycles offer therapeutic potential for challenging targets due to their unique binding properties.
• Accurate prediction of macrocyclic conformational ensembles is crucial for rational drug design.
• Molecular dynamics (MD) simulations are powerful but depend heavily on the accuracy of the force field used.

Purpose of the Study:

• To benchmark four different force fields for their ability to predict macrocyclic conformational ensembles.
• To evaluate the performance of replica exchange with solute tempering (REST2) simulations for macrocycles.
• To compare computational predictions with experimental data from nuclear Overhauser effect (NOE) experiments.

Main Methods:

• Performed REST2 simulations on 11 macrocyclic compounds.
• Compared simulated conformational ensembles against experimental NOE distance restraints.
• Evaluated force fields including OpenFF 2.0, XFF, GAFF2, and OPLS/AA.

Main Results:

• Modern force fields OpenFF 2.0 and XFF demonstrated good performance in predicting macrocyclic ensembles.
• These modern force fields outperformed older force fields like GAFF2 and OPLS/AA.
• Identified specific cases where all tested force fields failed to accurately reproduce experimental data.

Conclusions:

• REST2 simulations combined with modern force fields can frequently yield accurate macrocyclic compound ensembles.
• The selection of an appropriate force field is critical for reliable computational predictions in macrocyclic drug design.
• Further advancements in force fields are needed to address limitations in predicting certain macrocyclic conformational behaviors.