Welcome to the John Mitchell research group
Ph.D. studentships in Applying Systems Biology to Address Diseases of Metabolism and Allosteric Inhibitors of Enzymes are now available in St Andrews.
We are an Informatics and Computational Chemistry research group. Our group is based in the Purdie building on the North Haugh in St Andrews, but Richard is located at the Unilever Centre in Cambridge.
Research areas we are interested in are enzyme catalysis, protein-ligand interactions, molecular evolution and structural bioinformatics, computational toxicology, prediction of solubility and other molecular properties, and the classification of drugs used for doping in sport.
Here are presentations describing our recent work on Predicting the Mechanism of Phospholipidosis, RF-Score: A new scoring function for Protein-Ligand affinity prediction, The Chemistry of Protein Catalysis and In silico calculation of aqueous solubility.
We have developed the MACiE database of enzyme reaction mechanisms. Have a look at MACiE and read the accompanying Open Access paper in Nucleic Acids Research.
Thanks to our Sponsors
News
Predicting the mechanism of phospholipidosis is published as an Open Access paper in the Journal of Cheminformatics (26 Jan 2012).
Read the story of our iGEM jamboree in Amsterdam on the SULSA website (26 Jan 2012).
MACiE version 3.0 is released.
St Andrews wins a second successive gold medal at the 2011 iGEM European Jamboree in Amsterdam!
Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier is published in the Journal of Chemical Information and Modeling.
The review article Informatics, machine learning and computational medicinal chemistry has now been published in Future Medicinal Chemistry.
There's a new page describing our scoring function RF-Score. It's free of charge to all users, enjoy!
St Andrews wins a gold medal at iGEM 2010.
Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P is currently listed in the Top 20 most cited articles in the Journal of Chemical Information and Modeling for the last three years (16 Nov 2010).
Predicting Intrinsic Aqueous Solubility by a Thermodynamic Cycle is currently listed in the Top 20 most cited articles in Molecular Pharmaceutics for the last three years (16 Nov 2010).
Predicting Phospholipidosis Using Machine Learning is published as an Open Access paper in Molecular Pharmaceutics under the ACS Author Choice scheme. We pay, you read.
John is an advisor for the St Andrews iGEM team 2010, 2011 and 2012.
The Protein Ligand Database (PLD) is available online as a spreadsheet.
You may be interested in our publications (e.g. scoring functions, odour prediction), or a compilation of resources that we use in our work (datasets containing structures and properties, list of EC-PDB-CATH correspondences, etc.).
Teaching material for CH1202, ID1004, CH3441, CH5714 and SUPACCH.