4.8 Article

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

Journal

ACS CATALYSIS
Volume 9, Issue 3, Pages 2313-2323

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acscatal.8b04043

Keywords

Sterimol; descriptors; conformation; multivariate modeling; enantioselectivity

Funding

  1. EPSRC Centre for Doctoral Training in Synthesis for Biology and Medicine [EP/L015838/1]
  2. AstraZeneca
  3. Diamond Light Source
  4. Defence Science and Technology Laboratory
  5. Evotec
  6. GlaxoSmithKline
  7. Janssen
  8. Novartis
  9. Pfizer
  10. Syngenta
  11. Takeda
  12. UCB
  13. Vertex
  14. National Science Foundation [ACI-1532235, ACI-1532236, ACI-1548562]
  15. University of Colorado Boulder and Colorado State University
  16. Extreme Science and Engineering Discovery Environment (XSEDE) [TG-CHE180056]

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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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