UCB and Microsoft have struck a new multi-year, strategic collaboration to combine Microsoft’s computational services, cloud, and AI with UCB’s drug discovery and development capabilities.
As several drug discovery activities require the analysis of high-dimensional data sets or multi-modal unstructured information, Microsoft’s platform can support UCB’s scientists, including its data scientists, to discover new medicines in a more efficient and innovative way.
The collaboration builds on the work UCB and Microsoft have already embarked on around COVID-19. As part of the COVID Moonshot project, UCB’s medicinal and computational chemists contributed compound designs to this worldwide open-science project to create an orally bioavailable anti-viral for COVID-19 – with the most potent series of compounds coming from UCB designs.
This combination of cutting-edge science, computing power, and AI algorithms aims to significantly accelerate the iteration cycles required to explore a vast chemical space to test many hypotheses and identify more effective molecules.
The collaboration plans to extend this model and identify other areas where computing power, AI, and science can accelerate the development of life changing therapies for people living with severe diseases in immunology and neurology.
The work will augment UCB’s scientists, subject matter experts and research partners across every part of the drug discovery and delivery value chain by harnessing diverse research information and AI models alongside human expertise and creativity.
Jean-Christophe Tellier, CEO of UCB, said: “By amplifying the power of scientific innovation through digital transformation, we hope to have a better understanding of what makes a patient’s journey unique so that we can provide personalised and differentiated medicine in a sustainable way.”
UCB and Microsoft will explore how to combine diverse research data sets with four strategic objectives in mind, allowing UCB to: improve a patient’s overall journey; increase the impact of a treatment through a deeper understanding of the biological causes of the diseass; systematically provide better research data-driven insights to enable the faster discovery of therapeutic molecules; accelerate clinical development timelines.