Queen Mary University of London has agreed a new collaborative research project with AstraZeneca to advance AI for drug discovery. The research will be led by Professor Greg Slabaugh, Director of Queen Mary's cross-disciplinary Digital Environment Research Institute.
The researchers will develop and refine algorithms for multimodal data integration. These algorithms integrate various types of omic data—such as RNA, protein, and imaging—from pharmacologically or genetically modified cells. The goal is to expedite small compound drug discovery by identifying the most informative omics technologies, creating reference datasets, and maximising value from AstraZeneca's ongoing efforts in medicinal chemistry.
To do this, Queen Mary will apply their existing machine learning algorithms and academic expertise to AstraZeneca's multi-omic datasets. They will refine and develop them to create new omics analytical capabilities to look for ways to streamline the drug discovery process, making it faster and more cost-effective.
Queen Mary's Professor Greg Slabaugh comments:
"Artificial intelligence has significant potential to bring new treatments to more patients faster – so we're proud to be working with AstraZeneca to develop new machine learning platforms to accelerate drug discovery. By combining our academic expertise with industry data and biological knowledge, we hope to make a transformative impact for patients and the entire field."
It typically takes over ten years and more than $1bn to bring a new drug to market, so accelerating the process using AI has great potential to reduce time and costs. The Queen Mary-AstraZeneca AI tools will be developed to be useful across multiple applications and stages of the R&D pipeline.
The project starts in January 2025 and will run for 30 months. The funding is from AstraZeneca, UKRI, and Queen Mary.
