Bobbypaton/Sterimol: Sterimol Version 1.0

Lab, Paton; Jackson, Kelvin E.

doi:10.5281/zenodo.1320768

Cited by 2 publications

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“…We compared the experimental averaged A-values of a large number of groups with their B1 Sterimol parameter, as a measure of their steric bulk (Figure A). The (nonweighted) B1 Sterimol parameters were computed using Paton’s recent program, , from MP2/aug-cc-pVTZ optimized geometries of the groups (with an additional hydrogen to ensure closed-shell systems). For cases where the computed B1 Sterimol parameter was smaller than the vdW radius of the first atom of the group, it was corrected to equal this vdW radius, resulting in the corrected B1 Sterimol parameter, B1′.…”

Section: Resultsmentioning

confidence: 99%

London Dispersion Helps Refine Steric A-Values: Dispersion Energy Donor Scales

2021

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We suggest a scale of dispersion energy donors (DEDs) that allows for direct comparisons with steric effects. This scale is based on the classic A-values and allows groups to reorient to minimize strain, thereby providing an advantage over raw group polarizabilities. The A-value can no longer be considered purely a steric factor. Even for groups that do not participate in charge transfer or electrostatic interactions, the A-value includes Pauli repulsion (steric hindrance) and attractive London dispersion (LD) interactions. Although the common assumption is that, at the distances found in monosubstituted cyclohexanes, steric demands are the key factors influencing conformer preferences, we show in this computational study that there is a non-negligible LD part. We use this system to build a DED scale and a complementary steric scale. These scales are quantitatively comparable, as they are based on the same system, and allow for comparison of the two competing interactions in experimentally relevant settings. In addition, we show that LD interactions can be used to explain puzzling data regarding relative group sizes.

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Section: Resultsmentioning

confidence: 99%

London Dispersion Helps Refine Steric A-Values: Dispersion Energy Donor Scales

2021

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Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

et al. 2021

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Conspectus Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s we...

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Bobbypaton/Sterimol: Sterimol Version 1.0

Cited by 2 publications

References 0 publications

London Dispersion Helps Refine Steric A-Values: Dispersion Energy Donor Scales

London Dispersion Helps Refine Steric A-Values: Dispersion Energy Donor Scales

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

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