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AI for drug discovery

Working across Queen Mary faculties, DERI is building a strong base for AI-based drug discovery bioscience.

Researchers have been assembled with diverse backgrounds in bioinformatics, natural language processing, computer vision, computational chemistry, cell biology, molecular pharmacology, and related specialities.

Our vision is to explore new AI drug discovery strategies at the drug, target, and system levels to improve health and wellbeing across the lifecourse, while revealing the mechanisms underlying normal physiology.

Why are we setting up now?

In the UK, the average healthspan is not keeping pace with increases in lifespan. Understanding the ageing process and developing pharmaceutical interventions to promote healthy ageing are fundamental to transforming this future.

UK pharmaceutical companies have a key role to play, but the current drug discovery process is widely regarded as slow and inefficient. On average, it costs approximately $1.3 billion and ten years to bring a new therapeutic drug to market, and this cost is expected to increase. Unfortunately, this trend is exacerbating to the point of becoming unsustainable as discovery of new drugs faces the increasingly high bar of the efficacy of the existing pharmacopeia and an increasingly stringent regulatory environment.

However, by leveraging scientific advances in computational processing, AI can play a role to reduce drug discovery costs and timelines by leveraging large scientific databases, evaluating potential drug candidates in silico, and accelerating high content screening assays through automated data analysis.

AI as a national priority

It is well recognised that there is a pressing AI skills gap in the UK, with bioscience research a major tool in supporting emerging industries. National government policy is preparing the UK to be at the forefront of this AI revolution; promoting the UK base as the best place for AI companies to flourish and deploying AI benefits across all sectors of society. The Office for Artificial Intelligence is engaging with industry and academia to set out a National AI Strategy that ensures the UK remains a global leader and influencer.

At national level it is recognised that UK universities have the expertise to significantly develop technology. Queen Mary is highly placed in this ambition with its leading global reputation in research, which is a benchmark for DERI to be in an innovative centre for unlocking the power of data.

Unique data assets and expertise

Our academics have a strong history of collaboration. They led on the planning and implementation of Genomics England, an initiative to initially sequence 100,000 genomes from NHS patients, allowing data linkage and analysis to improve treatments and save lives.

We are also home of the East London Genes & Health programme, a longitudinal study of people of Bangladeshi and Pakistani ethnicity within the east London community. The study looks at the genetic makeup of volunteers to help researchers understand more about the nature of disease in the community.

Queen Mary also has other unique data assets: we can access our extensive BBSRC-funded molecular biology research within our Faculty of Medicine and Dentistry and the School of Biological and Chemical Sciences (SBCS), which encompasses a wide range of model systems including yeasts, plants, invertebrates, amphibians, birds and human cell lines. This provides a rich environment to train students on a variety of validation studies involving plant, animal and human health. We can also access The Centre for Predictive in vitro Models (CPM); researchers across QM are using these in vitro models  to examine fundamental hypotheses around health and disease.

The BBSRC-funded Centre for Cell Dynamics  and the Centre for Structural Biology within SBCS host Cryo-Electron, super-resolution and high-resolution live-cell microscopes, collating 100s of Terabytes of image datasets at atomic, molecular and subcellular resolution, supporting world-class discovery research in the biosciences.

Academic groups

Academic groups include, but are not limited to:

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