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基于物理的有机分子溶解度预测

Physics-Based Solubility Prediction for Organic Molecules.

作者信息

Fowles Daniel J, Connaughton Benedict J, Carter James W, Mitchell John B O, Palmer David S

机构信息

Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow, Scotland G1 1XL, U.K.

EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Scotland KY16 9ST, U.K.

出版信息

Chem Rev. 2025 Aug 13;125(15):7057-7098. doi: 10.1021/acs.chemrev.4c00855. Epub 2025 Jul 29.

Abstract

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.

摘要

准确预测有机分子的水溶性在从能源材料的设计与制造到评估潜在污染物的环境影响等一系列领域都极为重要。这对制药行业尤为重要,因为低水溶性问题常常阻碍新药的研发。溶解度的实验测量被广泛应用,但往往耗时、资源密集且仅适用于已合成的分子。因此,需要开发计算方法来预测溶解度。近年来,基于物理的方法取得了显著进展,有几种不同的技术能够准确预测溶解度并提供大量用于结构优化的热力学数据。在此,我们让读者全面了解这些基于物理的溶解度预测方法的理论背景和实际应用。这包括对每种方法的各种优缺点的讨论,以及对持续研究领域的说明。还讨论了评估溶解度的实验方法和数据驱动方法,以提供背景信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f693/12355698/f83763b299a1/cr4c00855_0001.jpg

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