Our goal is to develop computational methods for predicting antimicrobial resistance.


The Modernising Medical Microbiology (MMM) group in Oxford, of which we are a part, is pioneering genetics-based clinical microbiology. The central idea is to infer which antibiotics can be used to treat an infection by examining the mutations in the genome and looking up their effect in a catalogue of previously-seen cases. To achieve this goal (1) accurately mapping which mutations confer resistance through large-scale genomic sampling projects and (2) developing predictive methods that can deal with novel or rare mutation.

There are two main approaches for making a prediction:

  1. Machine-learning (induction). These are able to learn patterns from a large dataset and able to predict whether novel mutations confer resistance or not.
  2. Molecular dynamics simulations (deduction). These are physics-based and use molecular simulation to calculate how the binding free energy of the antibiotic changes upon introduction of a specific protein mutation. If the mutation significantly reduces how well the antibiotic binds, then we deduce that it confers resistance.

The second physics-based approach could be used in the development of new antibiotics (or the modification of existing ones) to determine how many mutations allow the bacteria to escape the action of the drug. Minimising this number should, we hope, prolong the lifespan of an antibiotic. Of course, the latter uses 4-6 orders of magnitude more computational power than the former.