Predicting drug solubility in different solvents using molecular simulation and machine learning at University of Strathclyde on FindAPhD.com

Dr M Jorge , Dr C Price Applications accepted all year round Self-Funded PhD Students Only

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Glasgow United Kingdom AI & Machine Learning Biochemistry Bioinformatics Chemical Engineering Computational Chemistry Manufacturing Pharmaceutical / Medicinal Chemistry Pharmacy / Pharmaceutics Physical Chemistry

Department of Chemical and Process Engineering, University of Strathclyde

About the Project

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 [1]. 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 [2], and advanced machine learning techniques, which can provide sufficient accuracy in a much shorter time frame [3]. 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 Center 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:

http://www.strath.ac.uk/engineering/chemicalprocessengineering

http://www.strath.ac.uk/courses/research/chemicalprocessengineering/

Funding Notes

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.

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.

References

[1] Brown et al. “Enabling precision manufacturing of active pharmaceutical ingredients: workflow for seeded cooling continuous crystallisations”, Mol. Syst. Des. Eng. 2018,3, 518.
[2] Garrido, N. M. et al. “Using Molecular Simulation to Predict Solute Solvation and Partition Coefficients in Solvents of Different Polarity”, Phys. Chem. Chem. Phys., 2011, 13, 9155.
[3] Palmer et al. “Random forest models to predict aqueous solubility”, J. Chem. Inf. Model. 2007, 47, 150.

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

University of Strathclyde Department of Chemical and Process Engineering

About the Project

Funding Notes

References