Introducing the BenevolentAI and UCL PhDs
At BenevolentAI, we use AI to improve drug discovery in order to bring safe and effective new medicines into routine clinical practice more quickly for the benefit of patients. Our discovery platform uses cutting edge AI technology to identify novel drug targets, design new drug molecules, find the patients most likely to benefit from them and design the most efficient clinical trials. We collaborate with many academic scientists in leading institutions around the world such as UCL and these close relationships form a vital part of how we develop and use our platform.
Two areas of particular focus at Benevolent are the application of machine learning to human genetics for drug target discovery and validation and the application of real world clinical data to identify and characterise groups of patients most likely to benefit from novel drugs. The UCL Centre for Translational Genomics (CTG) and Institute for Health Informatics (IHI) are international centres of excellence for research in these two areas and we are excited about further strengthening our already established links with them both.
Human genetics provides a unique opportunity for causal inference within complex biological systems using observational data from large numbers of people. Causal biological links can be inferred between genes that encode potential drug targets and many thousands of human traits. This allows us to generate a data representation of the human being as a model organism in health and disease - an invaluable asset when seeking novel drug targets. The data model is both large and information-rich and provides an ideal substrate for machine learning models to identify and characterise relationships between drug targets, disease and the pathophysiological process that link them. Adding this deeper level of understanding contributes to increasing the likelihood a proposed drug target is pursued into a successful clinical trial. Our PhD studentship with UCL CTG will investigate how genetic and machine learning methods can be brought together to achieve this aim.
UCL IHI is an internationally-recognised leader in using real world electronic health records generated during patient care, such as those available through the CALIBER resource, for research to improve human health and healthcare. These large and complex data offer large amounts of valuable health-related information which can provide novel insights into how disease first develops, how it progresses, what other diseases occur at the same time and why patients might have different disease progression patterns. Our studentship with IHI will focus on using machine learning and artificial intelligence methods on these complex data in order to identify, characterize and validate subgroups of patients with complex, chronic disease characterised by the trajectory of their disease course over time. These insights can lead to new understanding of the underlying biological and pathophysiological mechanisms of disease and potentially help identify novel drug targets.
Gaining deeper insights into the subtypes of heterogeneous diseases allows us to investigate the biological process underlying each subtype and to develop medicines for the group of patients in whom they are most likely to provide clinical benefit. This studentship will support the development and application of methods in very large clinical datasets to find and characterise such subgroups in order that drug discovery can be undertaken for clearly defined patient groups.
We hope that the students undertaking these PhDs will enjoy and thrive on close interaction with an organisation like Benevolent where they can see the true translational potential of their work. Training the next generation of experts is critical to turning data and analytical methods into new medicines. We are looking forward to working closely with our colleagues at UCL and our students to do this.
Daniel Swerdlow, Drug Discovery Physician