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.

中文翻译：

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催化剂对映选择性预测建模的多实例学习方法

在这里，我们报告了多实例学习方法在手性催化剂对映选择性预测建模中的应用。催化剂由捕获分子立体构型的 pmapper 物理化学描述符编码的构象集合表示。每个催化的化学反应都被转化为一个浓缩的反应图，生成了 ISIDA 片段描述符。这种方法不需要任何构象对齐，并且可以潜在地用于带有不同支架的多种催化剂。其效率已在预测 BINOL 衍生的磷酸催化剂在不对称硫醇加成到 N-酰基亚胺中的选择性方面得到证明，并以先前报道的模型为基准。