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

通过化学计量学和计算化学的结合预测有机溶剂中的溶解度

基本信息

批准号：
1939697
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1939697
关键词：
Solubility prediction organic solvents through

项目摘要

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.

溶解度是评价活性药物成分效力的一项基本性质。在药物合成中，溶解度通过预测加工和纯化的成本来帮助中间体的纯化和合成路线的选择是至关重要的。目前大多数预测模型依赖于实验数据，要么作为热力学值，要么作为结构碎片的参数。这使得半经验模型可以调整和补偿其假设中的固有错误。然而，当新化合物以与提供半经验参数的化合物完全不同的结构模式制成时，即在已知的化学空间之外，问题就出现了。学生将在解决化学知识的这一紧迫差距方面发挥主导作用。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。统计工具将用于分析这些性质，并将它们与实验溶解度联系起来。随着溶解度影响因素的了解，溶解度预测模型将随之发展。重要的是，DFT方法并不局限于已知的化学空间。这将使该项目迅速扩展到新的化学领域，并为后期的实验验证提供预测。