The diverse range of activation energies obtained from our screenings illustrates the versatility of our approach in exploring and understanding the kinetic landscape of bioorthogonal chemistry. This diversity is vital for constructing extensive datasets that can significantly enhance the predictive accuracy of machine learning models aimed at reaction prediction and optimization.