Samuel Boobier
- Email: cmsbo@leeds.ac.uk
- Thesis title: Solubility prediction through a combination of chemometrics and computational chemistry
Profile
I am a first year PhD student in School of Chemistry having graduated from University of St Andrews in June 2017 with Master in Chemistry (1st class honours). My PhD focuses on building predictive models for solubility, with special emphasis on how these models can be used in pharmaceutical industry. Statistical analyses are performed on data from a variety of sources, including electronic structure method calculations, and the resulting models are built with QSPR & cheminformatics. Previous research highlights include: How human and artificial intelligence can be used to predict solubility; theoretical modelling of the interaction energies of chiral surfaces; transition state modelling of alpha-deprotonation of peptides; and paleodietary analysis of 5th century human remains from Vesuvius area of Italy. I am funded by a EPSRC CASE scholarship with AstraZeneca.
Publications:
Can human experts predict solubility better than computers?
S. Boobier, A. Osbourn & J.B.O. Mitchell
Journal of Cheminformatics, 9:63 (2017)
doi: 10.1186/s13321-017-0250-y
Research interests
- Cheminformatics
- Statistical modelling
- Quantum mechanical modelling (DFT)
- Solubility prediction
- Drug design, process & development
Qualifications
- Master in Chemistry, (MChem) University of St Andrews