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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

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...

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

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

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.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Conformations of Ethane and Propane

Conformations of Ethane and Propane

In an organic molecule, free rotation about the carbon-carbon single bond results in energetically different conformers of the molecule. Due to this rotation, called the internal rotation, ethane has two major conformations — staggered and eclipsed.
Staggered conformation is a low energy and more stable conformation with the C-H bonds on the front carbon placed at 60°dihedral angles relative to the C-H bonds on the back carbon, leading to a reduced torsional strain. In staggered...

Conformations of Cyclohexane

Conformations of Cyclohexane

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...

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Related Experiment Video

Updated: Aug 24, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.

Kuzma Khrabrov¹, Ilya Shenbin², Alexander Ryabov^3,4

¹AIRI, Kutuzovskiy prospect house 32 building K.1, Moscow, 121170, Russia. kadurin@airi.net.

Physical Chemistry Chemical Physics : PCCP

|October 24, 2022

Summary

Machine learning models struggle with multi-molecule quantum chemistry calculations, showing decreased accuracy and poor generalization. Larger datasets are crucial for developing better machine learning models in computational chemistry.

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Last Updated: Aug 24, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

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Published on: November 12, 2014

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational Quantum Chemistry
Materials Science
Drug Discovery

Background:

Electronic wave function calculation is essential for determining molecular properties but is computationally intensive using traditional methods like Hartree-Fock and Density Functional Theory (DFT).
Machine learning (ML) offers a potential solution to reduce computational costs for approximating wave functions.

Purpose of the Study:

Introduce a new large-scale dataset of electron structures for drug-like molecules.
Establish a benchmark for evaluating molecular property estimation in a multi-molecule setting.
Assess the performance of various ML methods in computational quantum chemistry.

Main Methods:

Development of a curated dataset of molecular electron structures.
Creation of a novel benchmark for multi-molecule property estimation.
Evaluation of diverse ML models against the established benchmark.

Main Results:

Existing ML models exhibit significant accuracy degradation when transitioning from single-molecule to multi-molecule computations.
ML models demonstrate limited generalization capabilities across different chemical classes.
Larger datasets demonstrably improve the performance of ML models in quantum chemistry applications.

Conclusions:

Current ML approaches require further development to effectively handle multi-molecule scenarios in computational chemistry.
Generalizability and robustness of ML models across diverse chemical systems remain key challenges.
The size and quality of training datasets are critical factors for advancing ML in quantum chemistry.