  The mechanistic underpinnings of asymmetric catalysis at atomic levels
provide shortcuts for developing the potential value of chiral catalysts beyond
the current state-of-the-art. In the enantioselective redox transformations,
the present intuition-driven studies require a systematic approach to support
their intuitive idea. Arguably, the most systematic approach would be based on
the reliable quantitative structure-selectivity relationship of diverse and
dissimilar chiral scaffolds in an optimal feature space that is universally
applied to reactions. Here, we introduce a predictive workflow for the
extension of the reaction scope of chiral catalysts across name reactions. For
this purpose, whole geometry descriptors were encoded from DFT optimized 3D
structures of multiple catalyst scaffolds, 113 catalysts in 9 clusters. The
molecular descriptors were verified by the statistical comparison of the
enantioselective predictive classification models built from each descriptors
of chiral iodoarenes. More notably, capturing the whole molecular geometry
through one hot encoding of split three-dimensional molecular fingerprints
presented reliable enantioselective predictive regression models for three
different name reactions by recycling the data and metadata obtained across
reactions. The potential use value of this workflow and the advantages of
recyclability, compatibility, and generality proved that the workflow can be
applied for name reactions other than the aforementioned name reactions (out of
samples). Furthermore, for the consensus prediction of ensemble models, this
global descriptor can be compared with sterimol parameters and noncovalent
interaction vectors. This study is one case showing how to overcome the
sparsity of experimental data in organic reactions, especially asymmetric
catalysis.
