Development of molecular-solvent interaction descriptors to improve accuracy of Quantitative Structure Retention Relationship models

色谱保留的最新预测依赖于市售分子描述符，如VolSurf+3D、Dragon、莫伊。这些描述符可用于评价分子结构-活性或结构-性质关系，以及用于分子数据库的相似性分析和高通量筛选。用于计算这些描述符的算法通常不考虑色谱移动的相的组成。例如，色谱移动的相通常由缓冲液/有机溶剂混合物组成，其影响分析物的离子状态并增强或抑制与固定相的特异性相互作用。虽然疏水相互作用是主要保留机制，但通常是次要相互作用决定结构相似化合物的洗脱顺序。该提案的目标包括开发新的分子描述符，其性能优于目前可用的选项，因为：a）考虑与色谱移动的相更可比的介质b）考虑与水性缓冲液组分的离子配对c）区分异构体化合物，例如位置异构体或非对映异构体溶剂化描述符将使用分子液体的积分方程理论（IETML）。IETML是一种来自统计力学的溶剂化模型，它提供了丰富的准确的溶液相数据，并且对于高通量筛选来说足够快。该方法隐含地区分异构体化合物，并适用于广泛的纯/混合溶剂系统，包括那些含有离子物种。它已成功地用于计算溶剂化自由能、渗透率、溶解度、分配系数和药物样分子的结合自由能[Chem. Rev.，2015，115，6312]。溶剂化自由能密度函数和热力学参数将计算为不同的溶剂组成和温度的矩阵的每种分析物。这些溶剂化描述符将用于训练新的定量结构保留关系模型（QSRR）。我们将遵循类似于用于预测Caco-2渗透性的程序[Mol. Pharmaceutics，2015，12，3420]，但使其适应色谱溶剂系统和环境条件。该大学开发的所有软件包将在项目结束时提供给辉瑞公司。

State of the art predictions of chromatographic retention rely on commercially available molecular descriptors such as VolSurf+3D, Dragon, MOE. These descriptors can be used to evaluate molecular structure-activity or structure-property relationships, as well as for similarity analysis and high-throughput screening of molecule databases. The algorithms that are used to calculate these descriptors usually do not take the composition of chromatographic mobile phase into account. For example, chromatographic mobile phase often consists of buffer/organic solvent mixtures which affect the ionic state of analytes and enhance or suppress specific interactions with the stationary phase. Although hydrophobic interactions are the primary retention mechanism, it is often the secondary interactions which determine the elution order of structurally similar compounds. The objectives of this proposal include the development of novel molecular descriptors that outperform currently available options by virtue of: a) considering a medium more comparable with the chromatographic mobile phaseb) taking into consideration ion pairing with aqueous buffer componentsc) differentiating between isomeric compounds e.g. positional isomers or diastereomers Solvation descriptors will be developed using the integral equation theory of molecular liquids (IETML). IETML is a solvation model from statistical mechanics that provides a wealth of accurate solution-phase data, and is fast enough for high-throughput screening. The method implicitly differentiates between isomeric compounds, and is applicable to a wide-range of pure/mixed solvent systems, including those containing ionic species. It has been used successfully to compute solvation free energies, permeabilities, solubilities, partition coefficients, and binding free energies of druglike molecules [Chem. Rev., 2015, 115, 6312]. Solvation free energy density functions and thermodynamic parameters will be computed for each analyte for a matrix of different solvent compositions and temperatures. These solvation descriptors will be used to train novel Quantitative Structure Retention Relationship models (QSRR). We will follow a procedure similar to one used to predict Caco-2 permeability [Mol. Pharmaceutics, 2015, 12, 3420], but adapt it to chromatographic solvent systems and environmental conditions. All software packages that are developed by the University will be provided to Pfizer at the end of the project.