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

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.