Accurate prediction of aqueous solubility for organic molecules is of great importance across a range of fields, from the design and manufacturing of energy materials, to assessing the environmental impact of potential pollutants. It is of particular significance to the pharmaceutical industry, in which problems with low aqueous solubility frequently hamper the development of new drugs. Experimental measurements of solubility are used extensively, but are often time-consuming, resource intensive and only applicable to already synthesized molecules. As such, there is a need for the development of computational approaches to predict solubility. In recent years, there have been considerable advances in physics-based methods, with several contrasting techniques able to give accurate predictions of solubility and a wealth of thermodynamic data for structural optimization. Here, we provide the reader with a thorough understanding of the theoretical background and practical applications of these physics-based methods to predict solubility. This includes discussions of the various advantages and disadvantages of each approach, and an indication of areas of continuing research. Experimental and data-driven methods to assess solubility are also discussed to provide context.
Fowles et al. (Tue,) studied this question.