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/

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.

Machine Learning, Artificial Intelligence & informatics in Chemistry; Prediction of solubility and other molecular thermodynamic properties; Modelling the organic crystalline state; Classification and computer-based representation of enzyme reaction mechanisms; Bioinformatics studies of molecular evolution; Modelling protein-ligand interactions.

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).