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Synlett 2021; 32(18): 1833-1836
DOI: 10.1055/a-1553-0427
cluster
Machine Learning and Artificial Intelligence in Chemical Synthesis and Catalysis

Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity

D. Zankov
a   Laboratory of Chemoinformatics, University of Strasbourg, 4, B. Pascal, 67081 Strasbourg, France
c   Laboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University, Kremlyovskaya 18, 420008 Kazan, Russia
,
P. Polishchuk
b   Institute of Molecular and Translational Medicine, Palacký University, Hnevotinska 5, 77900 Olomouc, Czech Republic
,
T. Madzhidov 
c   Laboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University, Kremlyovskaya 18, 420008 Kazan, Russia
,
A. Varnek∗
a   Laboratory of Chemoinformatics, University of Strasbourg, 4, B. Pascal, 67081 Strasbourg, France
d   Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan
› InstitutsangabenDZ thanks the French Embassy in Russia for the PhD fellowship. TM thanks Russian Science Foundation (Grant No. 19-73-10137) for the support.
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Abstract

Here, we report an application of the multi-instance learning approach to predictive modeling of enantioselectivity of chiral catalysts. Catalysts were represented by ensembles of conformations encoded­ by the pmapper physicochemical descriptors capturing stereoconfiguration of the molecule. Each catalyzed chemical reaction was transformed to a condensed graph of reaction for which ISIDA fragment descriptors were generated. This approach does not require any conformations’ alignment and can potentially be used for a diverse set of catalysts bearing different scaffolds. Its efficiency has been demonstrated in predicting the selectivity of BINOL-derived phosphoric acid catalysts in asymmetric thiol addition to N-acylimines and benchmarked with previously reported models.

Key words

asymmetric catalysis - chemoinformatics - machine learning - QSSR


Publikationsverlauf

Eingereicht: 10. Juni 2021

Angenommen nach Revision: 16. Juli 2021

Accepted Manuscript online:
16. Juli 2021

Artikel online veröffentlicht:
12. August 2021

© 2021. Thieme. All rights reserved

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