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

Giuseppe Antinucci, Busra Dereli, Antonio Vittoria, Peter H. M. Budzelaar, Roberta Cipullo, Georgy P. Goryunov, Pavel S. Kulyabin, Dmitry V. Uborsky, Luigi Cavallo, Christian Ehm, Alexander Z. Voskoboynikov, Vincenzo Busico

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)6934-6945
Number of pages12
JournalACS Catalysis
StatePublished - May 31 2022

ASJC Scopus subject areas

  • Catalysis


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