School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, England.
J Chem Inf Model. 2009 Nov;49(11):2572-87. doi: 10.1021/ci900286s.
The dissolution of a chemical into water is a process fundamental to both chemistry and biology. The persistence of a chemical within the environment and the effects of a chemical within the body are dependent primarily upon aqueous solubility. With the well-documented limitations hindering the accurate experimental determination of aqueous solubility, the utilization of predictive methods have been widely investigated and employed. The setting of a solubility challenge by this journal proved an excellent opportunity to explore several different modeling methods, utilizing a supplied dataset of high-quality aqueous solubility measurements. Four contrasting approaches (simple linear regression, artificial neural networks, category formation, and available in silico models) were utilized within our laboratory and the quality of these predictions was assessed. These were chosen to span the multitude of modeling methods now in use, while also allowing for the evaluation of existing commercial solubility models. The conclusions of this study were surprising, in that a simple linear regression approach proved to be superior over more complex modeling methods. Possible explanations for this observation are discussed and also recommendations are made for future solubility prediction.
化学物质在水中的溶解是化学和生物学的基本过程。化学物质在环境中的持久性和在体内的作用主要取决于水溶解度。由于有充分记录的限制因素阻碍了水溶解度的准确实验测定，因此广泛研究和采用了预测方法。本期刊提出的溶解度挑战为探索几种不同的建模方法提供了极好的机会，利用高质量的水溶解度测量数据集进行了研究。在我们的实验室中使用了四种截然不同的方法（简单线性回归、人工神经网络、类别形成和可在体内模型），并评估了这些预测的质量。这些方法涵盖了目前使用的多种建模方法，同时还可以评估现有的商业溶解度模型。这项研究的结论令人惊讶，因为简单线性回归方法被证明优于更复杂的建模方法。对这种观察结果的可能解释进行了讨论，并为未来的溶解度预测提出了建议。
