Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment

Abstract

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.

Prediction of transition state structures of gas-phase chemical reactions via machine learning

“…Therefore, the generation of multiple reaction pathways is essential for determining the most favorable reaction pathways. Furthermore, to design catalysts or retrosynthetic pathways, the exploration of many competitive reactions is frequently required 31 – 33 . To tackle this problem, the generation of multiple TS structures using fast and accurate ML inference is demonstrated with the aid of reactant and product sampling.…”

Prediction of Transition State Structures of General Chemical Reactions via Machine Learning

“…Furthermore, to design catalysts or retrosynthetic pathways, acquiring information on competitive reaction mechanisms is a challenging but demanding work. [31][32][33] To tackle this problem, generation of multiple TS structures the fast and accurate ML inferences is demonstrated with the aid of reactant and product sampling.…”