NIH Chemical Genomics Center, National Human Genome Research Institute, NIH, Bethesda, MD 20892-3370, USA.
Bioorg Med Chem. 2011 Jul 1;19(13):4127-34. doi: 10.1016/j.bmc.2011.05.005. Epub 2011 May 13.
Kinetic solubility measurements using prototypical assay buffer conditions are presented for a ∼58,000 member library of small molecules. Analyses of the data based upon physical and calculated properties of each individual molecule were performed and resulting trends were considered in the context of commonly held opinions of how physicochemical properties influence aqueous solubility. We further analyze the data using a decision tree model for solubility prediction and via a multi-dimensional assessment of physicochemical relationships to solubility in the context of specific 'rule-breakers' relative to common dogma. The role of solubility as a determinant of assay outcome is also considered based upon each compound's cross-assay activity score for a collection of publicly available screening results. Further, the role of solubility as a governing factor for colloidal aggregation formation within a specified assay setting is examined and considered as a possible cause of a high cross-assay activity score. The results of this solubility profile should aid chemists during library design and optimization efforts and represent a useful training set for computational solubility prediction.
本文呈现了使用典型检测缓冲条件对大约 58000 个小分子的库进行的动力学溶解度测量。根据每个分子的物理和计算性质对数据进行了分析，并考虑了这些趋势，以了解物理化学性质如何影响水溶解度的普遍观点。我们进一步使用决策树模型进行溶解度预测分析，并通过多维评估物理化学关系到溶解度的具体“突破常规者”相对于常见的定论。还根据每个化合物在公开可用的筛选结果集合中的交叉测定活性评分，考虑了溶解度作为测定结果决定因素的作用。此外，还研究了在特定测定环境中，溶解度作为胶态聚集形成控制因素的作用，并将其视为高交叉测定活性评分的可能原因。该溶解度谱的结果将有助于化学家在文库设计和优化工作中，并为计算溶解度预测提供有用的训练集。
