Bioinformatics and computational chemistry

Group leader: John Mitchell

Research overview

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/

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Related links

Research group website

Publications

Mitchell, JBO 2014, ' Machine learning methods in chemoinformatics ' Wiley Interdisciplinary Reviews: Computational Molecular Science , vol Early view.
Barker, D , Ferrier, DEK , Holland, PWH , Mitchell, JBO , Plaisier, H , Ritchie, MG & Smart, SD 2013, ' 4273π: bioinformatics education on low cost ARM hardware ' BMC Bioinformatics , vol 14, 243.
Mavridis, L , Nath, N & Mitchell, JBO 2013, ' PFClust: a novel parameter free clustering algorithm ' BMC Bioinformatics , vol 14, no. 213, 213.

Overview

Scientists associated with the thirty-two research groups that are affiliated with the Biomedical Sciences Research Complex perform highly innovative, multi-disciplinary research in eleven broad areas of biomedical research, employing state-of-the-art techniques to address key questions at the leading edge of the biomedical and biological sciences.

Follow the links on the left to view individual research groups associated with one or more of the eleven BSRC research areas.

Research areas

Scientists associated with the thirty-two research groups that are affiliated with the Biomedical Sciences Research Complex perform highly innovative, multi-disciplinary research in eleven broad areas of biomedical research, employing state-of-the-art techniques to address key questions at the leading edge of the biomedical and biological sciences.

Follow the links on the left to view individual research groups associated with one or more of the eleven BSRC research areas.

Research by academic schools

Research in the BSRC is conducted by thirty-two independent research groups based in the Schools of Biology, Chemistry, Physics and Astronomy, and Medicine. Follow the links on the left to view groups associated with each school.