Selection of Low-Dimensional 3‑D Geometric Descriptors for Accurate Enantioselectivity Prediction

This study focuses
on building enantioselectivity models using
only a few intuitively meaningful descriptors based on the “buried
volume” idea. Appropriate dissection of the sphere is used
to calculate the buried volume of quadrants and octants, which we
name %VBQ and %VBO. Propene polymerization catalysis to isotactic polypropylene
(iPP) and 1,1′-bis-2-naphthol (BINOL)-phosphoric acid-catalyzed
thiol addition to N-acyl imines are used to illustrate
the approach. For iPP, only a single steric descriptor derived from
the comparison of hindrance in differently occupied octants (Δ%VBO) is needed, and electronic effects are unimportant.
Moreover, the model (mean absolute deviation, MAD, 0.12 kcal/mol)
works for more than a single catalyst class, allowing in silico catalyst design. For thiol addition, the best performance is achieved
by comparison of hindrance in octants, and one steric descriptor is
needed (Δ%VBO) in addition to an
electronic descriptor, the natural population analysis (NPA) charge
on the P atom. In both cases, key ingredients are (a) the use of properly
chosen “scanning regions” (e.g., octants
or quadrants) and (b) the availability of highly accurate experimental
data sets. The low dimensionality of descriptor space and their obvious
intuitive meaning naturally provide guidelines for further catalyst
optimization.