Computational design of chiral organic catalysts for asymmetric synthesis is a promising technology that can significantly reduce the material and human resources required for the preparation of enantiopure compounds. Herein, for the modeling of catalysts' enantioselectivity, we propose to use the multi-instance learning approach accounting for multiple catalyst conformers and requiring neither conformer selection nor their spatial alignment. A catalyst was represented by an ensemble of conformers, each encoded by three-dimesinonal (3D) pmapper descriptors. A catalyzed reactant transformation was converted into a single molecular graph, a condensed graph of reaction, encoded by 2D fragment descriptors. A whole chemical reaction was finally encoded by concatenated 3D catalyst and 2D transformation descriptors. The performance of the proposed method was demonstrated in the modeling of the enantioselectivity of homogeneous and phase-transfer reactions and compared with the state-of-the-art approaches.

Chemistry Solutions Elsevier Ltd Oxford OX5 1GB United Kingdom

Institute for Chemical Reaction Design and Discovery Hokkaido University Sapporo 001 0021 Japan

Institute of Molecular and Translational Medicine Palacký University Olomouc 77900 Czech Republic

Laboratory of Chemoinformatics University of Strasbourg Strasbourg 67081 France

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