ABSTRACT

In this work we demonstrated, that machine learning opens a way for real design of ligands with required metal ion selectivity. We performed the ensemble QSPR modelling of the Li⁺/Na⁺ complexation selectivity and the stability constants for the Li⁺L and Na⁺L complexes of phosphoryl podands in nonaqueous solvent THF/СНCl₃ (4:1 v/v). The models were built and cross-validated using MLR with the ISIDA QSPR program and SVM with the libSVM package. The program SVMsmf was implemented to fulfil an ensemble modelling using libSVM and the Substructural Molecular Fragments (SMF) descriptors. SMF were used as descriptors for the ensemble modelling, properties predictions by consensus models and design of combinatorial library of new ligands. SMF such as the P=O group, the ether and P=O groups bound through the aromatic ring contribute significantly to the Li⁺/Na⁺ selectivity. The developed models were applied for the prediction of the studied properties for a focused virtual library of 3057 phosphoryl podands generated using SMF contributions promising for selective binding of lithium. Consensus models selected hits for a synthesis by combinatorial library screening. Among the constructed selective ligands – hits, three new podands were synthesized, for which the experimentally estimated selectivity is in satisfactory agreement with that predicted.

摘要

在这项工作中，我们证明了机器学习为真正设计具有所需金属离子选择性的配体开辟了道路。我们在非水溶剂 THF/СНCl ₃中对 Li ⁺ /Na ⁺络合选择性和磷酰基 podands的 Li ⁺ L 和 Na ⁺ L 络合物的稳定性常数进行了整体 QSPR 建模(4:1 v/v)。这些模型是使用带有 ISIDA QSPR 程序的 MLR 和带有 libSVM 包的 SVM 构建和交叉验证的。实现了 SVMsmf 程序以使用 libSVM 和子结构分子片段 (SMF) 描述符实现集成建模。SMF 被用作集成建模、通过共识模型的特性预测和新配体组合库设计的描述符。SMF，如 P=O 基团、醚和 P=O 基团通过芳环结合对 Li ⁺ /Na ⁺有显着贡献选择性。开发的模型用于预测使用 SMF 贡献生成的 3057 个磷酰 podands 的聚焦虚拟库的研究特性，有望选择性结合锂。共识模型通过组合库筛选为合成选择命中。在构建的选择性配体 - 命中中，合成了三个新的 podand，实验估计的选择性与预测的一致。