Drawing inspiration from linear free energy relationships, MLR models, pioneered by Norrby and coworkers and later further developed by Sigman and coworkers , are commonly used for the prediction of enantioselectivity. In such models, the substrates, catalysts, and other relevant reaction species are encoded via a suitable representation of expert-chosen descriptors. Subsequently, the target property of interest, commonly ∆∆G ‡ , is fitted to the representation via a linear fit of the form 𝑦 = 𝑚 ! 𝑥 ! + 𝑚 " 𝑥 " … + 𝑚 # 𝑥 # + 𝑘, where y is the target property, mn are the regression coefficients, k is the offset and x1, …, xn are the molecular descriptors. The regression coefficients are also indicative of the importance of the respective molecular parameter.