Multi-Instance Learning Approach to Predictive Modeling of Catalysts Enantioselectivity
| Autor: | Timur I. Madzhidov, Alexandre Varnek, Pavel G. Polishchuk, Dmitry V. Zankov |
|---|---|
| Přispěvatelé: | Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010405 organic chemistry
Chemistry Organic Chemistry Enantioselective synthesis 010402 general chemistry 01 natural sciences Combinatorial chemistry Chemical reaction 0104 chemical sciences Catalysis Cheminformatics Cluster (physics) Molecule Graph (abstract data type) Selectivity [CHIM.CHEM]Chemical Sciences/Cheminformatics |
| Zdroj: | SYNLETT SYNLETT, Georg Thieme Verlag, 2021, ⟨10.1055/a-1553-0427⟩ |
| ISSN: | 0936-5214 1437-2096 |
| DOI: | 10.1055/a-1553-0427⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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