Conformational Searching for Complex, Flexible Molecules

Brueckner, Alexander C.; Ogba, O. Maduka; Snyder, Kevin M.; Richardson, H. Camille; Cheong, Paul Ha‐Yeon

doi:10.1142/9781786344090_0005

Cited by 8 publications

(6 citation statements)

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“…Analogously to the state-of-the-art in computational NMR prediction, 26 wSterimol automates conformer generation, geometry optimization, filtering, and the final calculation of Sterimol values (Figure 2). First, all the potential conformers are generated with a conformational sampling method, 27 such as Monte Carlo, 28 molecular dynamics, 29 or SUMM (systemic unbounded multiple minimum). 30 Since systematic torsional exploration of substituents with a small number of rotatable bonds is exhaustive, we chose to implement a SUMM-like approach.…”

Section: Methodsmentioning

confidence: 99%

Conformational Effects on Physical-Organic Descriptors: The Case of Sterimol Steric Parameters

2019

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Mathematical relationships that relate chemical structure with selectivity have provided quantitative insights underlying catalyst design and informing mechanistic studies. However, flexible compounds can adopt several distinct geometries and can be challenging to describe, using a single structure-based descriptor. How best to quantify the structural characteristics of an ensemble of structure poses both practical and technical difficulties. In this work, we introduce an automated computational workflow that can be used to obtain multidimensional Sterimol parameters for a conformational ensemble of a given substituent from a single command. The Boltzmann-weighted Sterimol parameters obtained from this approach are shown to be useful in multivariate models of enantioselectivity, while the range of values from conformers within 3 kcal/mol of the most stable structure provides a visual way to capture a possible source of uncertainty arising in the resulting models for enantioselectivity. Our approach improves the model performance in cases where particularly flexible substituents have been studied. In all cases, this approach enables the impact of conformational effects on model performance to be quickly diagnosed: in particular, these effects may be more significant than statistical model error such that selectivity prediction should be performed more cautiously. Implementing our approach requires no programming expertise and can be executed from within a graphical user interface using open-source programs.

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Section: Methodsmentioning

confidence: 99%

Conformational Effects on Physical-Organic Descriptors: The Case of Sterimol Steric Parameters

2019

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“…In analogy to what is currently done at the state of the art of NMR prediction, 26 wSterimol automates conformer generation, geometry optimization, filtering, and the final calculation of Sterimol values (Figure 2). First, all the potential conformers are generated with a conformational sampling method 27 such as Monte-Carlo, 28 Molecular Dynamics, 29 or SUMM (Systemic Unbounded Multiple Minimum). 30 Since systematic torsional exploration of substituents with a small number of rotatable bonds is exhaustive, we chose to implement a SUMM-like approach.…”

Section: Methodsmentioning

confidence: 99%

Conformational Effects on Physical-Organic Descriptors – the Case of Sterimol Steric Parameters

Brethomé¹,

Fletcher²,

Paton

2018

Preprint

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<div> <div> <div> <p>Mathematical relationships which relate chemical structure with selectivity have provided quantitative insights underlying catalyst design and informing mechanistic studies. Flexible compounds, however, can adopt several distinct geometries and so can be challenging to describe using a single structure-based descriptor. How best to quantify the structural characteristics of an ensemble of structure poses both practical and technical difficulties. In this work we introduce an automated computational workflow which can be used to obtain multidimensional Sterimol parameters for a conformational ensemble of a given substituent from a single command. The Boltzmann-weighted Sterimol parameters obtained from this approach are shown to be useful in multivariate models of enantioselectivity, while the range of values from conformers within 3 kcal/mol of the most stable structure provides a visual way to capture a possible source of uncertainty arising in the resulting models. Implementing our approach requires no programming expertise and can be executed from within a graphical user interface using open-source programs. </p> </div> </div> </div>

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“…Thus, a movement has been made toward including raw data and error bars in publications to allow for better analysis and comparison of scientific data. , Even with excellent experimental data, the computational chemist is limited by time and resources. With the technology currently available, cyclopeptides with five or so residues represent a practical size limit for accurate DFT-based computations. − …”

Section: Current Best Practices and Known Sources Of Errormentioning

confidence: 99%

Quantum Chemistry Calculations for Metabolomics

et al. 2021

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A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e. , in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

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Conformational Searching for Complex, Flexible Molecules

Cited by 8 publications

References 0 publications

Conformational Effects on Physical-Organic Descriptors: The Case of Sterimol Steric Parameters

Conformational Effects on Physical-Organic Descriptors: The Case of Sterimol Steric Parameters

Conformational Effects on Physical-Organic Descriptors – the Case of Sterimol Steric Parameters

Quantum Chemistry Calculations for Metabolomics

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