Welcome to the John Mitchell research group
A joint Ph.D. studentship with the CCDC in computational chemistry entitled Using Crystal Data to Address the Solubility of Pharmaceutical Compounds is 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.
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.
Luna De Ferrari's talk on Active and Guided Learning of Enzyme Function from MLSB 2012, Basel, is available as a video online. There are also available presentations describing our recent work on Computational Enzymology, 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.
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News
Active and guided learning of enzyme function, Luna De Ferrari's talk from MLSB 2012, Basel, is available as a video online (2 May 2013).
Rosanna Alderson wins the Innovation Poster Prize at the 3rd Annual PhD Symposium on Computational Biology and Innovation at University College Dublin (7 Dec 2012)!
Our review paper Enzyme Informatics is published in Current Topics in Medicinal Chemistry.
St Andrews win a third successive iGEM gold medal and the Best New BioBrick Part, Natural prize (7 Oct 2012)!
Our paper Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification is published in Journal of the Royal Society Interface (29 Aug 2012).
The first St Andrews Bioinformatics Symposium was held on 22 August.
Congatulations to Richard on passing his PhD viva (10 Aug 2012).
First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules is published online as an ASAP article in J. Chem. Theory Comput. (25 Jul 2012).
Congatulations to Luna on passing her PhD viva (24 Jul 2012).
See John's slides from the May 2012 conference "Computational Chemogenomics to understand System Biology & Computational Medicinal Chemistry" in Geneva (23 May 2012).
Is EC class predictable from reaction mechanism? is published in BMC Bioinformatics, an Open Access journal (24 Apr 2012).
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, SUPACCH, and BL4273.