ACE is an MM-based software that predicts the stereochemical outcome of asymmetric reactions by modeling the relevant TSs of ligand/substrate/catalyst systems. The stereoinducing step for these reactions must be known a priori. Part of the larger VIRTUAL CHEMIST platform for asymmetric catalyst design, ACE is built on two fundamental organic chemistry principles: 1) the Hammond-Leffler postulate and 2) the Curtin-Hammett principle. The TSs are built in accordance with principle 1) (i.e., the TS is most similar to the species to which it is closest in energy, either reactants or products, hence is a linear combination of reactant and product structures), while the enantiomeric excess is calculated according to principle 2) (i.e., the %ee is determined according to the difference in energies between diastereomeric TSs). The preferred stereoisomers are determined through a genetic algorithm that efficiently samples the conformational space around the ligand/substrate/catalyst system. ACE has been successfully tested on seven organo-and metal-catalyzed reactions commonly employed in asymmetric synthesis: Diels-Alder cycloaddition (with chiral auxiliaries and organocatalysts), Aldol reaction, Shi epoxidation, OsO4-based dihydroxylation of alkenes, ZnEt2-addition to aldehydes, and Rh-catalyzed hydrogenation of enamides, achieving accuracies of ~ 1 kcal/mol compared to experimental values. Quantum-guided molecular mechanics (Q2MM). Q2MM is an MM-based methodology that uses automated FF parametrization to describe TSs and predict the outcome of stereochemical reactions. Similar to ACE, Q2MM is part of a larger catalyst design platform called CatVS. To date, CatVS/Q2MM has been primarily employed for organometallic catalysis, with the tested reactions involving Rh-catalyzed hydrogenation of enamides, OsO4 dihydroxylation of alkenes, ZnEt2-addition to aldehydes, Pd-catalyzed allylation, asymmetric redox-relay Heck reaction, and Ru-catalyzed hydrogenation of ketones. The FFs generated by Q2MM are reaction-specific and are known as TSFFs. Similar to ACE, the stereoinducing step must be known a priori. However, in contrast to ACE, Q2MM relies on reference data for a training set of model TSs that is subjected to QM calculations to determine the necessary parameters for FF parametrization. The uniqueness of Q2MM relies on the usage of the QM-derived Hessian matrix (i.e., the variations in energy with respect to geometry changes) to fit TSFF force constants for bonded parameters. Once the TSFF has been generated and validated for a reaction, Monte Carlo (MC) conformational searches are employed to find the relevant TSs and stereoisomers. The %ee's are calculated by Boltzmann-averaging the relative energies of the identified conformations. Q2MM has been tested on four metal-catalyzed reactions, achieving correlation coefficients between 0.8-0.9 between predicted and experimental data. An Automated Reaction Optimizer for New catalysts (AARON). In contrast to both ACE and Q2MM, AARON is an open-source framework that interfaces various tools for structural manipulation, TS searches, and energy calculations. However, like ACE and Q2MM, AARON is part of a larger toolkit named QChASM. Designed with ease-of-use in mind, AARON uses a library of TS templates to construct TSs of novel ligand/substrate/catalyst systems, followed by TS optimization at a desired level of theory (semiempirical methods or DFT). Once the TSs have been located, conformational sampling is performed using a rule-based methodology that accounts for the torsional preferences of each substituent. These conformers are then subjected to thermochemistry calculations to obtain free energies, which are Boltzmann-averaged over the populations of conformers leading to specific enantiomers to predict the %ee. Representative applications of AARON include Pd-catalyzed Heck allenylation, Rhcatalyzed hydrogenation of enamides, and the Lewis-base promoted propargylation of aromatic aldehydes. performed. Remarkably, CatVS/Q2MM was able to distinguish between highly stereoselective and poorly stereoselective ligands. For one substrate, the four most stereoselective ligands were among the top 5 predictions. Interestingly, the use of implicit solvent was not found to improve the accuracy, in line with what was observed with ACE. Stereoselectivity and catalytic activity optimization. A key aspect of reaction optimization is tuning the enantioselectivity without compromising reactivity. This is especially important in the synthesis of active pharmaceutical ingredients (APIs), as the final product has strict purity requirements. For this reason, high yields of the desired product with low catalyst loading are of significance. An interesting application of this concept has been developed by Dotson et al., who designed a computational workflow to fine-tune enantioselectivity while simultaneously accounting for catalyst/ligand reactivity in two metal-catalyzed reactions: 1) Pd-catalyzed Hayashi-Heck and 2) Rh-catalyzed alkene hydroformylation. These reactions use chiral bisphosphine ligands and are pharmaceutically relevant due to their use in the synthesis of a transient receptor potential ankyrin 1 (TRPA1) antagonist (Figure , Compound 20). The Dotson workflow began with assembling a set of over 550 chiral bisphosphine ligands, for which steric, electronic, and geometric descriptors were calculated with QM. For each reaction, a subset of ligands was selected for experiments to determine the regio-, enantioselectivity, and reaction yields/conversion. The latter was used to discriminate between reactive and unreactive complexes using two classification algorithms: a) a single-node decision tree for reaction 1) and b) a logistic regression classification algorithm for reaction 2). Consequently, for each reaction, the descriptors and associated experimental data of the reactive complexes were used to train a reaction/metal-agnostic MLR model capable of correlating input data to regio-and enantioselectivity. To verify whether the workflow can be used to prospectively screen for high conversion/high enantioselectivity ligands, the last step involved a virtual screen on the database of ligands not involved in training of the classification or MLR models. Applying the developed classification and MLR models on this database led to the identification of several ligands with excellent experimental conversion and enantioselectivity (Figure ). The goals of this work were to 1) describe the components of the reactions using descriptors that are agnostic to the mechanism and 2) develop a predictive SVR model without an assumption of a shared mechanism between the different data points. The final model related the catalyst structure to its function (enantioselectivity), in any reaction catalyzed by the input catalyst scaffold.