Frank von Delft
Professor of Structural Chemical Biology
My overall research project is to establish methods to ensure that X-ray structures can serve as a routine and predictive tool for generating novel chemistry for targeting proteins - as opposed to them being only occasionally and retrospectively useful descriptively, as is currently generally the case.
I am head of the Protein Crystallography SRF of the Centre for Medicines Discovery (CMD), which grew from the Oxford site of the Structural Genomics Consortium. I am also, jointly, Principal Beamline Scientist of beamline I04-1 at Diamond Light Source synchrotron (Harwell). After my PhD in protein crystallography with Tom Blundell in Cambridge, I have focused on methodology and high throughput techniques for crystallography, first in San Diego (academically at JCSG, and industrially at Syrrx, Inc), and since 2004 at the SGC, where my group helped solve over 650 crystal structures of human proteins.
As structural biologist, I am trying to help reshape how protein structure determination transforms rational drug design, by developing and making the new methodologies and tools available through platforms and products to ensure they are widely and routinely used by researchers world-wide. My long-term programme is to shrink by two orders of magnitude the time and cost required to develop small molecule inhibitors, by combining national facilities, artificial intelligence, robotics and cloud-based open access science, in order to make the bespoke design of inhibitors a consistently cheap, fast and widely-used approach in biology and medicine.
In late 2012 I also joined Diamond, to configure beamline I04-1 into a user facility for routine fragment screening by X-ray structures: this XChem facility has since supported large numbers of discovery projects from academia and industry. Accordingly, my research addresses the challenges both the upstream and downstream of the fragment screen: how to generate crystals that are suitable for such screening, and afterwards, how to proceed routinely from such a screen to compounds with biologically relevant affinity.
The research approach is two-pronged: developing the infrastructure (robotics, hardware, beamline) required to drive the throughput necessary for identifying the most appropriate samples at each step in the experimental process; and improving the methodology available at each step.
During the COVID-19 pandemic, I co-founded the COVID Moonshot, and antiviral drug discovery now forms part of my active research.
Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search.
Wills S. et al, (2023), J Chem Inf Model
Discovery and Development Strategies for SARS-CoV-2 NSP3 Macrodomain Inhibitors.
Schuller M. et al, (2023), Pathogens, 12
CoPriNet: graph neural networks provide accurate and rapid compound price prediction for molecule prioritisation
Sanchez-Garcia R. et al, (2023), Digital Discovery
The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges
Wills S. et al, (2022)
Fragment Libraries Designed to Be Functionally Diverse Recover Protein Binding Information More Efficiently Than Standard Structurally Diverse Libraries
Carbery A. et al, (2022), Journal of Medicinal Chemistry