People
John Mitchell (Principal Investigator)
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. They have developed novel applications of machine learning in computational biochemistry, such as drug side effect prediction, and identifying athletic performance enhancers.
John’s current research interests include studies of AI in both chemistry education and research, as well as the use of synthetic data for chemical machine learning.
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Ben Connaughton (PhD student, joint with Michael Bühl)
Ben Connaughton graduated with an MChem degree (First Class) from the University of St Andrews in 2022. He is in the penultimate year of his PhD, which is jointly supervised by Dr John Mitchell and Professor Michael Bühl, and funded by EaSI-CAT.
Ben uses a variety of computational techniques, including molecular docking, molecular dynamics simulations, QM/MM energy calculations, and machine learning, to explore the substrate scope of a recently-discovered enzyme. He collaborates with The Goss Lab at the University of St Andrews and the Mey Research Group at the University of Edinburgh.
Publications:
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Lily Bates (PhD student, joint with Dr Rafael da Silva)
Lily Bates is a PhD student co-supervised by Dr Rafael Guimaraes da Silva (School of Biology) and Dr John Mitchell (School of Chemistry). She graduated with a BSc in Biochemistry from the University of St Andrews in 2025, completing her final year research project in the da Silva Lab studying the ATP phosphoribosyltransferase enzyme (AbATPPRT) from Acinetobacter baumannii, an ESKAPE pathogen.
Started in Autumn 2025, she is continuing to study AbATPPRT in her PhD project, combining experimental and computational techniques to understand the allosteric regulation of AbATPPRT and develop novel AbATPPRT inhibitors.
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Rob Kennedy (MChem Materials Chemistry project student)
Rob Kennedy’s project assesses the chemical applicability of LLMs, and their accuracy in molecular property prediction.
Rob is investigating where the field currently stands, and whether the future belongs to LLMs or if older ML models, i.e. cheminformatics, are likely to remain useful. He employs supervised ML algorithms such as tree-based Random Forest and k-nearest neighbours on curated datasets, and compares the results to LLM predictions.
He also plans to use DFT as a benchmark for some applicable properties, where experimental data is not suitable.
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