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Related Concept Videos

Conformations of Cyclohexane02:11

Conformations of Cyclohexane

15.2K
Cyclohexane does not exist in a planar form due to the high angle and torsional strain it would experience in the planar structure. Instead, it adopts non-planar chair and boat conformations.
The chair form is the most stable and derives its name from its resemblance to the “easy chair.” In the chair conformation, two carbon atoms are arranged out-of-plane — one above and one below, minimizing the torsional strain. In the chair form, the bond angle is very close to the ideal...
15.2K
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

1.3K
At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
1.3K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.0K
VSEPR Theory for Determination of Electron Pair Geometries
45.0K
Conformations of Cycloalkanes02:29

Conformations of Cycloalkanes

14.1K
Adolf von Baeyer attempted to explain the instabilities of small and large cycloalkane rings using the concept of angle strain — the strain caused by the deviation of bond angles from the ideal 109.5° tetrahedral value for sp3  hybridized carbons. However, while cyclopropane and cyclobutane are strained, as expected from their highly compressed bond angles, cyclopentane is more strained than predicted, and cyclohexane is virtually strain-free. Hence, Baeyer’s theory that...
14.1K
Chair Conformation of Cyclohexane02:02

Chair Conformation of Cyclohexane

17.9K
The chair conformation is the most stable form of cyclohexane due to the absence of angle and torsional strain. The absence of angle strain is a result of cyclohexane’s bond angle being very close to the ideal tetrahedral bond angle of 109.5° in its chair conformer. Similarly, the torsional strain is also absent owing to the perfectly staggered arrangement of bonds.
The hydrogen atoms linked to carbons are arranged in two different axial and equatorial orientations to achieve this...
17.9K
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

1.7K
The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
1.7K

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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
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Conformational Analysis of Macrocyclic Compounds Using a Machine-Learned Interatomic Potential.

Hani M Hashim1, Jeremy N Harvey1

  • 1Department of Chemistry, KU Leuven, Celestijnenlaan 200f, 3001 Leuven, Belgium.

Journal of Chemical Theory and Computation
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning, using a graph neural network, accurately analyzes macrocyclic compound conformations. This approach overcomes challenges in predicting structures and energies, matching density functional theory results.

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Molecular Modeling

Background:

  • Macrocyclic compounds are crucial in chemistry and biology.
  • Conformational analysis of these molecules is computationally challenging.
  • Accurate prediction of molecular structures and energies is vital.

Purpose of the Study:

  • To develop and validate a machine-learned interatomic potential (MLIP) for macrocycle conformational analysis.
  • To assess the MLIP's accuracy against high-level quantum chemical methods.
  • To integrate the MLIP into sampling methods for efficient structure prediction.

Main Methods:

  • Training a Nequip-like graph neural network MLIP on DFT energy differences.
  • Using ωB97XD3 and GFN1-xTB levels of theory for training data.
  • Employing metadynamics-based conformational sampling with the CREST framework.

Main Results:

  • The MLIP accurately reproduced DFT-level relative conformer energies.
  • Optimized structures from MLIP closely matched DFT results.
  • Conformational sampling with MLIP recovered DFT-optimized structures from crystal data.

Conclusions:

  • Machine learning, specifically MLIPs, offers a powerful solution for macrocycle conformational analysis.
  • This approach significantly enhances the efficiency and accuracy of molecular modeling.
  • The developed MLIP shows great promise for studying complex macrocyclic systems.