Depending on the desired outcome, different methods can be used to relate the catalyst (and substrate, in some cases) structure to enantioselectivity. For example, Sigman and co-workers have been developing statistical models based on linear free energy relationships (LFER), where they aim to achieve both an increase in prediction accuracy as well as an intuitive understanding of the potential mechanism. This type of model aims to find a linear correlation between a variable and free energy, which in asymmetric catalysis is often the energy difference between the diastereomeric TSs (ΔΔG ‡ ). Since stereoselectivity is dependent on multiple variables, the model would usually be a multivariate linear regression (MLR). The input would be the numerical representations of the reaction components (descriptors), and the output would be the free energy difference. These models have been successfully applied on several metal catalyzed reactions such as the Pd-catalyzed enantioselective aryl-carbonylation of sulfonimidamides, Pd-catalyzed Hayashi-Heck reaction, Negishi coupling, and different Pd-and Ni-catalyzed crosscoupling reactions. Additionally, they have been applied to organocatalyzed reactions including the Mannich reaction, chiral phosphoric acid catalyzed nucleophilic addition to iminiums, HBD catalyzed addition of nucleophiles to nitro alkenes, and others. A different type of model used successfully in asymmetric catalysis is support vector regression (SVR).