Predictive Multivariate Models for Bioorthogonal Inverse-Electron Demand Diels-Alder Reactions

Inverse-electron demand Diels-Alder cycloadditions have emerged as important bioorthogonal reactions in chemical biology. Understanding and predicting reaction rates for bioconjugation reactions is fundamental for evaluating their efficacy in biological systems. Here, we present multivariate models to predict the second order rate constants of bioorthogonal inverse electron demand Diels-Alder reactions involving 1,2,4,5-tetrazines derivatives. A data-driven approach was used to model these reactions by parametrizing both the dienophiles and the dienes partners. The models are statistically robust and were used to predict/extrapolate the outcome of several reactions as well as to identify mechanistic differences among similar reactants.