Research Fellow in Data-driven Co-crystal Design

Are you an expert in co-crystal design and control? Would you like to be part of a team that does both experiments and theoretical modelling on pharmaceuticals? Would you like to train on cutting edge synchrotron X-ray scattering and data-driven analysis methods?

Only 1 in 5,000 drugs reach the market, mainly due to solubility issues that reduces their bioavailability and makes them less efficacious. One of the main routes the pharmaceutical industry uses to mitigate this issue is co-crystallisation, to get the right solid form that can be formulated easily. However, co-crystal design is currently a trial-and-error process that would benefit from data-driven approaches to narrow down possible co-formers. To enhance the predictive capabilities of models, it is vital to have knowledge of both the solution-state and the solid-state, drawn from both experimental and computational approaches. 

In this project, you will develop high-throughput methodologies to improve our understanding of the interplay between the solution-state dynamics and the solid-state in the co-crystallisation of small-molecule organics. This will involve: preparing meticulous datasets of solubility information for known drug co-crystals; studying the solution-state dynamics with synchrotron total X-ray scattering studies; and density functional theory based formation energies. You will also have the opportunity to train on cutting-edge physics-informed machine learning methods, in collaboration with the STFC Scientific Machine Learning (SciML) group. 

You will join Dr Anuradha Pallipurath (Royal Society University Research Fellow and Leeds UAF) and her team, funded by an EPSRC New Investigator Award that runs for a period of three years. The project, titled “Intelligent Engineering of multicomponent drug crystals”, requires both experimental and computational skills for successful delivery. The position is for two years in the first instance, to tailor to the skill set requirement of the project. If you also have experience with computational methods and/or are willing to train to carry out modelling studies, the position can be extended to the third year. As well as the STFC SciML team, the project will involve industrial collaborators from Astra Zeneca and the Cambridge Crystallographic Data Centre, which will provide great networking opportunities for career progression.

We are open to discussing flexible working arrangements.

To explore the post further or for any queries you may have, please contact: 

Dr Anuradha R. Pallipurath, Royal Society Olga Kennard Fellow (URF) and University Academic Fellow

E-mail: A.R.Pallipurath@leeds.ac.uk

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