Postgraduate research opportunities Predicting drug solubility in different solvents using molecular simulation and machine learning


Key facts

  • Opens: Wednesday 21 February 2024
  • Number of places: 1
  • Duration: 3 years


This project aims to develop a new computational tool, combining molecular simulations and machine learning approaches, to predict the solubility of complex pharmaceutical molecules in a variety of solvents.
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Students applying should have (or expect to achieve) a minimum 2.1 undergraduate degree in a relevant engineering/science discipline, and be highly motivated to undertake multidisciplinary research.

THE Awards 2019: UK University of the Year Winner
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Project Details

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.

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Funding details

This PhD project is initially offered on a self-funding basis. It is open to applicants with their own funding, or those applying to funding sources. However, excellent candidates will be eligible to be considered for a University scholarship.

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Dr Miguel Jorge

Senior Lecturer
Chemical and Process Engineering

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Professor Chris John Price

Chair In Industrial Crystallisation
Chemical and Process Engineering

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Number of places: 1

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Chemical and Process Engineering

Programme: Chemical and Process Engineering

Start date: Oct 2023 - Sep 2024

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