Old Road Campus Research Building
Frank von Delft
Professor of Structural Chemical Biology
Frank's 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.
He is jointly Principal Investigator of the Protein Crystallography group in the SGC (Oxford University), as well as Principal Beamline Scientist of beamline I04-1 at Diamond Light Source synchrotron (Harwell). After his PhD in protein crystallography with Tom Blundell in Cambridge, he has 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 his group has to date helped solve over 600 crystal structures of human proteins.
As structural biologist, he is seeking to 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. His 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 he set up the partnership with Diamond, in order to configure beamline I04-1 as a user facility for routine medium-throughput fragment screening by X-ray structures: this facility is now live to users from academia and industry. Accordingly, his focus and collaborations at the SGC address both ends of this facility: how to generate crystals that are suitable for such screening, and afterwards, how to proceed routinely from such a screen to compounds with good 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.
Rapid optimisation of fragments and hits to lead compounds from screening of crude reaction mixtures
Baker LM. et al, (2020), Communications Chemistry, 3
Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions
Scantlebury J. et al, (2020), Journal of Chemical Information and Modeling, 60, 3722 - 3730
Deliberately Losing Control of C-H Activation Processes in the Design of Small Molecule Fragment Arrays Targeting Peroxisomal Metabolism.
Kahn Tareque R. et al, (2020), ChemMedChem
Crowdsourcing drug discovery for pandemics.
Chodera J. et al, (2020), Nature chemistry, 12
Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions
Scantlebury J. et al, (2020)