Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity

D. Zankov; P. Polishchuk; T. Madzhidov; A. Varnek

doi:10.1055/a-1553-0427

Synlett, Inhaltsverzeichnis

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

^aLaboratory of Chemoinformatics, University of Strasbourg, 4, B. Pascal, 67081 Strasbourg, France

^cLaboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University, Kremlyovskaya 18, 420008 Kazan, Russia

,

P. Polishchuk

^bInstitute of Molecular and Translational Medicine, Palacký University, Hnevotinska 5, 77900 Olomouc, Czech Republic

,

T. Madzhidov

^cLaboratory of Chemoinformatics and Molecular Modeling, Kazan Federal University, Kremlyovskaya 18, 420008 Kazan, Russia

,

A. Varnek∗

^aLaboratory of Chemoinformatics, University of Strasbourg, 4, B. Pascal, 67081 Strasbourg, France

^dInstitute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, 001-0021 Sapporo, Japan

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Abstract

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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

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Referenzen

References
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