Although there are a number of computational approaches available for the aqueous solubility prediction, a majority of those models rely on the existence of a training set of thermodynamic solubility measurements or/and fail to accurately account for the lattice packing contribution to the solubility. The main focus of this study is the validation of the application of a physics-based aqueous solubility approach, which does not rely on any prior knowledge and explicitly describes the solid-state contribution, in order to guide the improvement of poor solubility during the lead optimization. A superior performance of a quantum mechanical (QM)-based thermodynamic cycle approach relative to a molecular mechanical (MM)-based one in application to the optimization of two pharmaceutical series was demonstrated. The QM-based model also provided insights into the source of poor solubility of the lead compounds, allowing the selection of the optimal strategies for chemical modification and formulation. It is concluded that the application of that approach to guide solubility improvement at the late discovery and/or early development stages of the drug design proves to be highly attractive.

中文翻译：

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使用基于物理的建模为改善溶解度提供指导线索优化。

尽管有许多计算方法可用于水溶性预测，但是这些模型中的大多数都依赖于热力学溶解度测量训练集的存在或/和未能准确说明晶格堆积对溶解度的贡献。这项研究的主要重点是验证基于物理的水溶性方法的应用，该方法不依赖任何先验知识，并明确描述了固态贡献，以指导改善铅生产过程中不良的溶解性优化。证明了基于量子力学（QM）的热力学循环方法相对于基于分子力学（MM）的方法在两种药物系列优化中的优越性能。基于质量管理的模型还提供了对铅化合物溶解性差的根源的见解，从而可以选择化学修饰和配制的最佳策略。结论是，在药物设计的后期发现和/或早期开发阶段，该方法用于指导溶解度改善的应用被证明具有很高的吸引力。