Overriding Innate Decomposition Temperatures of an Avibactam Prodrug Precursor Using Data Science-Guided Synthesis

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 analogue with a higher onset
temperature, allowing for an overall safer reagent and proof of concept
for the workflow. Interpretation 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.