Predicting the solubility of complex drug-like molecules is crucial at several stages of the drug discovery and manufacture. In particular, solvent selection has been highlighted as a crucial step in process design and optimisation. Although historically this issue has been addressed by the pharmaceutical industry through experimental solubility measurements, these are both time-consuming and material hungry, limiting their breadth of application in solvent screening. Consequently, there is a pressing need for computational models that can predict drug solubility accurately and efficiently, as this would accelerate the early stages of pharmaceutical process development.

This project aims to develop a new computational tool to predict the relative solubility of complex multifunctional drug molecules in a wide variety of solvents, including pure liquids, mixtures, supercritical fluids, new “green” solvents like ionic liquids or deep eutectics, and even hypothetical, not yet synthesised solvents. We will achieve this through an innovative combination of molecular modelling, which can predict solvation of small molecules very accurately, and advanced machine learning techniques, which can provide sufficient accuracy in a much shorter time frame. By combining the best of physics-based and data-based approaches, the method will strike the right balance between accuracy and computational speed to allow use in an industrial context, while having a strong physical basis to enable rational decision-making. The PhD project will be run in close collaboration with experimental colleagues at Strathclyde’s Centre for Continuous Manufacture and Crystallisation (CMAC), and will suit a highly motivated, creative and independent student, preferably with experience in the use of computational modelling methods.

In addition to undertaking cutting edge research, students are also registered for the Postgraduate Certificate in Researcher Development (PGCert), which is a supplementary qualification that develops a student’s skills, networks and career prospects.

Information about the host department can be found by visiting:

• Department of Chemical & Process Engineering