Research Interest

Objective Target Prioritisation for Cancer Drug Discovery

A major problem often encountered in drug discovery is the abundance of biologically tantalizing targets that end up difficult to progress into useful drugs. These problems can be due to inherent ‘undruggability’ of a particular target, problems with the chemical matter that can be developed for it, or severe issues with selectivity.

We develop and apply computational methodologies for the objective assessment and prioritisation of molecular targets. These use empirically defined criteria and machine-learning algorithms to score the tractability of a target based on independent assessments such as ligand-based, structure-based, sequence-based and network-based assessments.

We apply these methodologies to prioritise potential targets from numerous sources including cancer genome sequencing, RNA interference studies and gene expression data. By combining these approaches with biological and chemical knowledge we are able to identify ‘low hanging fruit’ in a particular program, identify suitable targets for follow up and validation and suggest alternative intervention points for undruggable targets that are in cancer-causing pathways.