Dr John B. O. Mitchell
Reader
Biography
John Mitchell has a PhD in Theoretical Chemistry from Cambridge. He returned there from University College London in 2000, taking up a lectureship in Chemistry. He was appointed to a readership at St Andrews in 2009. His recent research has used computational techniques in pharmaceutical chemistry and structural bioinformatics. His group have worked extensively on prediction of bioactivity, solubility, melting point and hydrophobicity from chemical structure, using both informatics and theoretical chemistry methodologies. Recently they have developed novel applications of machine learning in computational biochemistry, such as drug side effect prediction, and identifying athletic performance enhancers.
Teaching
Lecturer CH5714 Chemical Applications of Electronic Structure Calculations; Lecturer CH4431 Scientific Writing; Lecturer CH3717 Statistical Mechanics and Computational Chemistry; Convenor & Tutor, CH1202 Introductory Chemistry; Lecturer ID1003 Great Ideas 1; Lecturer ID1004 Great Ideas 2; Tutor CH2701 Physical Chemistry 2; Tutor CH1401 Introductory Inorganic and Physical Chemistry; Lecturer SUPACCH Computational Chemistry (Postgraduate course).
Research areas
The interface between biology and chemistry is fertile ground for the development of new computational techniques. Yet it is still hard to predict protein-ligand binding, model protein folding or design effective pharmaceutical products.
Enzyme-catalysed reactions are ubiquitous and essential to the chemistry of life. Structures, gene sequences, mechanisms, metabolic pathways and kinetic data are currently spread between many different databases and throughout the literature. We have created MACiE, the world's first comprehensive electronic database of the chemical mechanisms of enzymatic reactions. We are using MACIE to investigate fundamental questions about the chemistry of enzyme functions, their evolution, and their substrate specificity.
Improving the prediction of solubility is essential to reduce the current unacceptable attrition rate in drug development. We are developing methods to predict aqueous solubility for drug-like molecules, and hope to move on to study its dependence on pH, salt effects and crystal polymorphism. We have developed a number of predictive methods for solubility, of which the most successful is based on a Random Forest of decision trees. We are also using computational chemistry to calculate the various energy terms associated with solvation. This work spans quantum chemistry, molecular simulation, QSAR and chemical informatics.
Additional information about the current Mitchell Group can be found here: http://chemistry.st-andrews.ac.uk/staff/jbom/group/
PhD supervision
- Benedict Connaughton
Selected publications
-
Open access
Can human experts predict solubility better than computers?
Boobier, S., Osbourn, A. & Mitchell, J. B. O., 13 Dec 2017, In: Journal of Cheminformatics. 9, 63Research output: Contribution to journal › Article › peer-review
-
Open access
Enzyme function and its evolution
Mitchell, J. B. O., Dec 2017, In: Current Opinion in Structural Biology. 47, p. 151-156Research output: Contribution to journal › Review article › peer-review
-
Open access
Probing the average distribution of water in organic hydrate crystal structures with radial distribution functions (RDFs)
Skyner, R. E., Mitchell, J. B. O. & Groom, C., 28 Jan 2017, In: CrystEngComm. 19, 4, p. 641-652 12 p.Research output: Contribution to journal › Article › peer-review
-
Open access
A review of methods for the calculation of solution free energies and the modelling of systems in solution
Skyner, R. E., McDonagh, J. L., Groom, C. R., van Mourik, T. & Mitchell, J. B. O., 17 Mar 2015, In: Physical Chemistry Chemical Physics. 17, 9, p. 6174-6191Research output: Contribution to journal › Article › peer-review
-
Open access
One origin for metallo-β-lactamase activity, or two? An investigation assessing a diverse set of reconstructed ancestral sequences based on a sample of phylogenetic trees
Alderson, R. G., Barker, D. & Mitchell, J. B. O., Oct 2014, In: Journal of Molecular Evolution. 79, 3-4, p. 117-129 13 p.Research output: Contribution to journal › Article › peer-review
-
Open access
Is experimental data quality the limiting factor in predicting the aqueous solubility of druglike molecules?
Palmer, D. S. & Mitchell, J. B. O., 4 Aug 2014, In: Molecular Pharmaceutics. 11, 8, p. 2962-2972 11 p.Research output: Contribution to journal › Article › peer-review
-
Open access
The natural history of biocatalytic mechanisms
Nath, N., Mitchell, J. B. O. & Caetano-Anolles, G., 29 May 2014, In: PLoS Computational Biology. 10, 5, 14 p., e1003642.Research output: Contribution to journal › Article › peer-review
-
Open access
Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules
McDonagh, J., Nath, N., De Ferrari, L., van Mourik, T. & Mitchell, J. B. O., 24 Feb 2014, In: Journal of Chemical Information and Modeling. 54, 3, p. 844-856 13 p.Research output: Contribution to journal › Article › peer-review
-
Open access
First-principles calculation of the intrinsic aqueous solubility of crystalline druglike molecules
Palmer, D. S., McDonagh, J. L., Mitchell, J. B. O., van Mourik, T. & Fedorov, M. V., 11 Sept 2012, In: Journal of Chemical Theory and Computation. 8, 9, p. 3322-3337 16 p.Research output: Contribution to journal › Article › peer-review
-
Open access
Artificial intelligence in pharmaceutical research and development
Mitchell, J. B. O., Jul 2018, In: Future Medicinal Chemistry. 10, 13, p. 1529-1531 3 p.Research output: Contribution to journal › Editorial › peer-review