While more descriptors are expected to provide a more complete representation of a reaction, they may also lead to overfitting, a common issue when developing ML or statistical models. As an indication of overfitting, the model performs well on the training set but poorly on the testing set, hence the need for significantly dissimilar training and testing sets to detect overtraining. Moreover, not all the descriptors will have a significant enough influence on the accuracy of the model and will add unnecessary noise. In general, the simpler the model (i.e., the fewer descriptors), the easier (and faster) it is to train and often the better (more generalizable) and interpretable the model will be. In practice, many descriptor combinations are evaluated for model training, and the most predictive set of descriptors is chosen for the final version of the model. However, a careful evaluation of the relevance of these descriptors should be carried out. Steric parameters. Steric effects play an important role in catalyst reactivity and selectivity. In the context of asymmetric catalysis, a catalyst may have different substituents, with each substituent taking up a different volume of the space around the reactive part of the catalyst, influencing the shape, stereoinduction, and reactivity of the catalyst. To account for these effects, descriptors representing the steric character of the molecule and its substituents have been developed. An important aspect of some steric parameters is that they are conformation dependent, making the selection of the conformation impactful. In practice, chemists often rely on C2 symmetrical catalysts (e.g., BINAP) to reduce the conformational space (e.g., the number of possible conformations, the number of different faces of nucleophilic attack), which in turn simplifies their optimization. Depending on the system, these descriptors may be calculated for the lowest energy conformer, the catalytically relevant conformer, or as a Boltzmann-weighted conformation average. Mentioned in text: Sterimol parameters, ASO, %buried volume. Geometric parameters. In addition to sterics, one can compute descriptors providing information on the 3D shape (geometry) of the molecule. The geometry of a catalyst affects the selectivity and rate of catalyzed reactions; therefore, the selection of the conformation is critical. In addition to overall shapes, geometric parameters may also contain information such as bond lengths or bond-angle and torsion values. Mentioned in text: cone angles. Electronic parameters. These descriptors aim to represent the electronic properties of the molecule, often of a reactive centre (e.g., the atoms participating in the reaction) or of a ligand (e.g., a phosphine ligand modulating the reactivity of metal centers). In organic chemistry, electronic properties describe information such as ability to donate or accept electrons (Lewis basicity and acidity, respectively), nucleophilicity and electrophilicity, hyperconjugation, and more. Unlike steric and geometric parameters, electronic parameters are often less influenced by conformation, and they can either be derived from a single (optimized) conformation or as a Boltzmann-weighted conformational average. Most electronic parameters describe a fragment of the molecule, such as a substituent or reactive atom, rather than the entire molecule. Mentioned in text: NBO charges, FMO gap. Empirical parameters. Descriptors do not always have to be computed theoretically. A variety of empirical descriptors can be used in model development, such as nuclear magnetic resonance (NMR) chemical shifts, NMR P tensors, and infrared (IR) spectrum frequencies and vibrations. Interaction Fields. Comparative molecular field analysis (CoMFA). A method developed for ligand 3D-QSAR studies in drug discovery, which has since been implemented in asymmetric catalysis modeling. The method aims to correlate reaction outcomes to molecular fields described by the steric and electronic properties of a molecule. To achieve this, molecules are first aligned and then placed in a three-dimensional energy grid. A probe atom is then added at strategic points on this grid and the interaction energy (Van der Waals and electrostatic) between the molecules and the probe atom is calculated at each grid point. These energies are the descriptors that input into regression models, most often partial least squares (PLS), which correlate catalytic activity/enantioselectivity with the computed descriptors. Comparative molecular similarity indices analysis (CoMSIA). CoMSIA is a 3D-QSAR method developed as a natural extension of CoMFA by including molecular similarity in the computation of the molecular fields. In addition to the steric and electronic parameters captured by CoMFA, CoMSIA also includes a hydrophobicity term. The calculation of descriptors is then performed in a similar manner to CoMFA.