Data science is emerging as a means to probe structureactivity relationships, design and analyze chemical space, and optimize chemical reactions, ultimately impacting a range of applications in the chemical enterprise. However, the computational infrastructure required to do so can be prohibitive to laboratories with experimental data but limited experience with and/or access to high performance computing (HPC) resources. To combat this issue, density functional theory (DFT)-level molecular descriptor libraries for commonly used substrates, 2, 3 ligands, and drug-like molecules have been constructed and disseminated to reduce redundancy of expensive calculations in the field. Descriptor libraries can be built agnostic to a particular reaction, and thus can be used in a range of applications; libraries of this type have been demonstrated to guide the selection of diverse reaction substrates, predict the outcome (e.g., selectivity, rate, or yield ) of chemical reactions, and elucidate key mechanistic features. In particular, we have found success applying atom-or bond-level descriptors focused on conserved moieties in each reaction component of a dataset, which are hypothesized to lend specific insight into the reactive site in order to maximize interpretability and mechanistic understanding.