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

Abstract

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.

Molecular-level insight in supported olefin metathesis catalysts by combining surface organometallic chemistry, high throughput experimentation, and data analysis

“…This was accomplished using the Sterimol parameter L sum , which quantifies steric volume along the axes of both ortho substitutes in aryloxide ligands. 41 Parameter L sum indicates different catalytic regimes for the respective subsets of ligands in the grafted formulations and is in line with our previous findings on respective molecular analogues (Fig. S8 † ).…”

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

“…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.…”

Machine Learning Transition Temperatures from 2D Structure

“…[50][51][52][53] On the other hand, macroscopic properties are more robust to higher-level or coarse-gained descriptors. [54][55][56][57][58] In the case of melting, for example, a molecular crystal may be identified by descriptors [59][60][61][62] derived from its repeating structural unit. [20] The mapping of a multi-molecule process from a single molecule is ambiguous however, thus emphasizing the importance of suitable descriptors.…”