Fast and accurate computational approaches to predicting
reactivity
in sulfa-Michael additions are required for high-throughput screening
in toxicology (e.g., predicting excess aquatic toxicity and skin sensitization),
chemical synthesis, covalent drug design (e.g., targeting cysteine),
and data set generation for machine learning. The kinetic glutathione
chemoassay is a time-consuming in chemico method used to extract kinetic
data in the form of log(
k
GSH
) for organic
electrophiles. In this work, we use density functional theory to compare
the use of transition states (TSs) and enolate intermediate structures
following C–S bond formation in the prediction of log(
k
GSH
) for a diverse group of 1,4 Michael acceptors.
Despite the widespread use of transition state calculations in the
literature to predict sulfa-Michael reactivity, we observe that intermediate
structures show much better performance for the prediction of log(
k
GSH
), are faster to calculate, and easier to
obtain than TSs. Furthermore, we show how linear combinations of atomic
charges from the isolated Michael acceptors can further improve predictions,
even when using inexpensive semiempirical quantum chemistry methods.
Our models can be used widely in the chemical sciences (e.g., in the
prediction of toxicity relevant to the environment and human health,
synthesis planning, and the design of cysteine-targeting covalent
inhibitors), and represent a low-cost, sustainable approach to reactivity
assessment.