Abstract
Statistical analysis is used to correlate the thermal decomposition temperature of diverse leaving groups of an avibactam
prodrug precursor. SMILES strings and Mordred calculated parameters were leveraged to provide a time-efficient workflow
for model development. The resulting models were deployed to predict a novel analog with a higher onset temperature,
allowing for an overall safer reagent and proof of concept for the workflow. Interperetation of the descriptors featured in
the models and subsequent DFT analysis uncovered univariate trends providing a deeper understanding of the
decomposition pathway. Finally, this workflow enabled the development of a predictive model correlating energy output of
the precursor analogs for a more comprehensive assessment.
Supplementary materials
Title
Supporting Information
Description
Includes experimental and computational details.
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Title
Parameters
Description
Mordred calculated and DFT-derived parameters used in study
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