Developing datasets. The first step in any modeling project is often data collection from the scientific literature (or generating data in the wet lab). Since representative systems are selected and modeled based on these data (ML) and/or used to test the method (QM, MM), data curation is essential, as literature data may be riddled with issues (e.g., misassigned stereocenters, incomplete experimental data, reproducibility issues). This process is a challenging task that may be prone to human error. Thus, model or method development and/or evaluation require the availability of consistent and reliable experimental enantioselectivities. The type and amount of data required to develop a predictive model depends on the approach used to model the reaction (evaluation on small sets or training on large sets) -Box 1.