Drug ranking using machine learning


Artificial intelligence relies on data – and lots of it. That’s a challenge when applying it to real world medical problems where every patient’s experience is different.

When selecting the best drug for an individual cancer patient, the data you have on the patient’s precise condition is likely to be incomplete.

That means any AI solution must be able to work with partial data.

Queen Mary’s DRUML technology can do just this for ranking cancer drugs. Cancer tumours differ dramatically from patient to patient, meaning that two people diagnosed with the same cancer may respond completely differently to the same therapy.

Invented by Prof. Pedro Cutillas at Barts Cancer Institute, DRUML is a methodology for building and integrating machine learning models, using ensembles of proteomic, phosphoproteomic and transcriptomic features to generate lists of ranked drugs based on their efficacy.

DRUML can make predictions without needing to compare to samples – a crucial requirement for the clinical implementation of machine learning and a core aim of precision medicine.

Using large-scale proteomics and phosphoproteomics in machine learning has never been systematically applied before. However, recent advances in proteomic techniques and a greater number of drug response profiles means we can now feed any type of omics data into machine learning models of drug response – advancing the field of precision medicine and bringing hope to the millions of people diagnosed with cancer around the world each year.

Alongside its clinical potential, the technology can also be used as a research tool to narrow down who should participate in a drug trial by predicting whether individual patients are likely to be responsive.

A patent has been filed and we’re actively looking for partners to license this technology to develop commercially.



Monika Hamilton PhD CLP –



Dr Pedro Cutillas, Barts Institute of Cancer