Solubility prediction in organic solvents through a combination of chemometrics and computational chemistry

在评价一种有效药物成分的效力时，溶解度是一个基本属性。在药物合成中，通过预测合成和提纯的成本，溶解度对中间体的提纯和合成路线的选择是至关重要的。大多数目前的预测模型依赖于实验数据，要么作为热力学数值，要么作为结构碎片的参数。这使得半经验模型能够调整和补偿其假设中的固有误差。然而，当新化合物的结构模式与用于提供半经验参数的结构模式完全不同时，即在已知的化学空间之外，问题就会出现。学生将在解决化学知识的这一紧迫缺口方面发挥带头作用。该项目将利用密度泛函理论(DFT)计算技术的最新进展来计算类药物化合物的性质。将使用统计工具来分析这些性质，并将它们与实验溶解度联系起来。因此，在了解影响溶解度的因素的情况下，将开发一个溶解度预测模型。重要的是，DFT方法并不局限于已知的化学空间。这将使该项目能够迅速扩展到新的化学空间，并为后期的实验验证提供预测。

Solubility is an essential property in evaluating an Active Pharmaceutical Ingredient's potency. In pharmaceutical syntheses, solubility is vital in assisting purification of intermediates and synthetic route selection by predicting the cost of work-up and purification. Most current prediction models rely on experimental data, either as thermodynamic values, or as parameters for structural fragments. These allow semi-empirical models to adjust and compensate for inherent errors in their assumptions. Problems, however, arise when novel compounds are made with completely different structural patterns to those used to provide the semi-empirical parameters, i.e. outside the known chemical space.The student will take a leading role in addressing this urgent gap in chemical knowledge. The project will exploit recent advances of Density Functional Theory (DFT) computational techniques to calculate properties of drug-like compounds. Statistical tools will be employed to analyse these properties and link them with experimental solubility. A model for solubility prediction will consequently be developed, with understanding of factors influencing solubility. Importantly, the DFT approach is not restricted to known chemical space. This will allow the project to quickly expand into novel chemical space, and provide predictions for experimental verification in the later stage.