Transforming Drug Discovery using Digital Biology
Abstract: Modern medicine has given us effective tools to treat some of the most significant and burdensome diseases. At the same time, it is becoming consistently more challenging and more expensive to develop new therapeutics. A key factor in this trend is that the drug development process involves multiple steps, each of which involves a complex and protracted experiment that often fails. We believe that, for many of these phases, it is possible to develop machine learning models to help predict the outcome of these experiments, and that those models, while inevitably imperfect, can outperform predictions based on traditional heuristics. To achieve this goal, we are bringing together high-quality data from human cohorts, while also developing cutting edge methods in high throughput biology and chemistry that can produce massive amounts of in vitro data relevant to human disease and therapeutic interventions. Those are then used to train machine learning models that make predictions about novel targets, coherent patient segments, and the clinical effect of molecules. Our ultimate goal is to develop a new approach to drug development that uses high-quality data and ML models to design novel, safe, and effective therapies that help more people, faster, and at a lower cost.
Date: 13 October 2022, 13:30 (Thursday, 1st week, Michaelmas 2022)
Venue: Big Data Institute, Old Road Campus OX3 7LF
Venue Details: Seminar Room 0
Speaker: Professor Daphne Koller (Instiro)
Organising department: Big Data Institute (NDM)
Part of: BDI seminars
Booking required?: Not required
Audience: Members of the University only
Editor: Graham Bagley