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

Brethomé, Alexandre; Fletcher, Stephen P.; Paton, Robert S.

doi:10.26434/chemrxiv.7125656.v1

Cited by 27 publications

(35 citation statements)

References 35 publications

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“…with perfect categorization of these molecules (Figure 2), where L is the Sterimol length (defined as the total distance following the primary axis of attachment, in Å), 23 SE is the solvation energy for the uncharged (0 charge dialkoxyarenes and +1 charge pyridiniums) and charged molecules (+1 charge dialkoxyarenes and 0 charge pyridiniums), and IE is the ionization energy of the solvated molecules (all energies in Hartree).…”

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confidence: 88%

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Cross-Platform Classifier of Chemical Stability for Charged Redoxmers

Silcox

Zhang

Tung

et al. 2021

ACS Materials Lett.

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The development of new redox flow battery chemistries is hampered by time-consuming organic syntheses and electrochemical characterization of candidate redoxmer molecules. Here, we use Sure Independence Screening and Sparsifying Operator (SISSO) to demonstrate a cross-platform classifier for chemical stability of charged redoxmers in electrolyte solutions. This SISSO model yields a single formula to separate positively charged dialkoxyarene catholytes and negatively charged pyridinium anolytes into stable and unstable species in acetonitrile-based electrolytes as probed through cyclic voltammetry measurements. Using such classifiers, the stability of new compounds across different synthetic platforms can be rapidly assessed.

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supporting

confidence: 88%

“…Steric parameters were calculated from optimized geometries in Sterimol. 23 SISSO with the dialkoxyarene molecules returned the model parameter ξ defined as…”

mentioning

confidence: 99%

Cross-Platform Classifier of Chemical Stability for Charged Redoxmers

Silcox

Zhang

Tung

et al. 2021

ACS Materials Lett.

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“…In most cases, descriptors are evaluated on a single ligand or substrate conformer, but for highly flexible ligands and substrates, weighted evaluation of 3D descriptors has been demonstrated. 434 In many of these examples, the QSPR models themselves serve to guide improvements to ligand chemistry.…”

Section: Data Mining Structures For Chemical Trendsmentioning

confidence: 99%

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

et al. 2021

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Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal−organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure−property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal−organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

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“…Since the Sterimol L term is a conformationally sensitive parameter, it may also describe the role of a preferred geometry. 18 Indeed, surveying the enantioselectivities of the reactions forming B and C, in which they differ only by RAE, shows that B performs better overall despite − i Pr appearing to be shorter than −CH 2 Bn. However, computation optimizations demonstrate that B can adopt more compact arrangements and smaller L values at the RAE than C, clarifying this nonintuitive trend.…”

Section: Journal Of the American Chemical Societymentioning

confidence: 99%

Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

et al. 2019

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The Minisci reaction is one of the most direct and versatile methods for forging new carbon–carbon bonds onto basic heteroarenes: a broad subset of compounds ubiquitous in medicinal chemistry. While many Minisci-type reactions result in new stereocenters, control of the absolute stereochemistry has proved challenging. An asymmetric variant was recently realized using chiral phosphoric acid catalysis, although in that study the substrates were limited to quinolines and pyridines. Mechanistic uncertainties and nonobvious enantioselectivity trends made the task of extending the reaction to important new substrate classes challenging and time-intensive. Herein, we describe an approach to address this problem through rigorous analysis of the reaction landscape guided by a carefully designed reaction data set and facilitated through multivariate linear regression (MLR) analysis. These techniques permitted the development of mechanistically informative correlations providing the basis to transfer enantioselectivity outcomes to new reaction components, ultimately predicting pyrimidines to be particularly amenable to the protocol. The predictions of enantioselectivity outcomes for these valuable, pharmaceutically relevant motifs were remarkably accurate in most cases and resulted in a comprehensive exploration of scope, significantly expanding the utility and versatility of this methodology. This successful outcome is a powerful demonstration of the benefits of utilizing MLR analysis as a predictive platform for effective and efficient reaction scope exploration across substrate classes.

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

Cited by 27 publications

References 35 publications

Cross-Platform Classifier of Chemical Stability for Charged Redoxmers

Cross-Platform Classifier of Chemical Stability for Charged Redoxmers

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

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