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Development of a ligand knowledge base, Part 1: Computational descriptors for phosphorus donor ligands

机译：配体知识库的开发，第1部分：磷供体配体的计算描述符

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A prototype collection of knowledge on ligands in metal complexes, termed a ligand knowledge base (LKB), has been developed. This contribution describes the design of DFT-calculated descriptors for monodentate phosphorus(m) donor ligands in a range of representative complexes. Using the resulting data, a ligand space is mapped and predictive models are derived for metal complexes. Important characteristics, including chemical, computational and statistical robustness for the generation and exploitation of such an LKB are described. Chemical robustness ensures transferability of the descriptors, as well as comprehensive sampling of ligand space. To make the calculations amenable to automation in an e-science setting, a reliable, well-defined computational approach has been sought from which the descriptors can be readily extracted. The LKB has been explored with multivariate statistical methods. Principal component analysis (PCA) is used for the mapping of chemical space, projecting multiple descriptors into scatter plots which illustrate the clustering of chemically similar ligands. Interpretation of the resulting principal components in terms of established steric and electronic properties and the importance of its statistical robustness to variations in the ligand set are discussed. Multiple linear regression (MLR) models have been derived, demonstrating the versatility of the descriptors for modeling varied experimentally determined parameters (bond lengths, reaction enthalpies and bond-stretching frequencies). The importance of re-sampling methods for testing the robustness of predictions is highlighted. A strategy for the construction of a robust LKB suitable for the modeling of ligand and complex behavior is outlined based on these observations.
机译：已开发出一种金属配位体中配体知识的原型集合，称为配体知识库（LKB）。该贡献描述了一系列代表性复合物中单齿磷（m）供体配体的DFT计算描述符的设计。使用得到的数据，可以绘制配体空间并导出金属络合物的预测模型。描述了重要的特性，包括化学，计算和统计的鲁棒性，这些LKB的生成和利用。化学稳健性确保了描述符的可转移性，以及对配体空间的全面采样。为了使计算适合于电子科学环境中的自动化，已寻求一种可靠的，定义明确的计算方法，可以从中容易地提取描述符。 LKB已使用多元统计方法进行了探索。主成分分析（PCA）用于绘制化学空间，将多个描述符投影到散点图中，这些散点图说明了化学上相似的配体的聚集。讨论了根据既定的空间和电子性质解释所得的主成分及其对配体组变化的统计稳健性的重要性。已经得出了多个线性回归（MLR）模型，证明了描述子用于对各种实验确定的参数（键长，反应焓和键拉伸频率）进行建模的通用性。强调了重新采样方法对于测试预测的鲁棒性的重要性。基于这些观察结果，概述了构建适合配体和复杂行为建模的健壮LKB的策略。

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