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Details of Grant 

EPSRC Reference: EP/S001077/1
Title: Bringing AI to structure-based compound optimisation by leveraging high-throughput X-ray crystallography in a structured data framework
Principal Investigator: Bradley, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
BenevolentAI Bio Ltd Diamond Light Source DIAMOND light source Ltd
F. Hoffmann-La Roche (International) GlaxoSmithKline plc (GSK) UCB
Department: Oxford Chemistry
Organisation: University of Oxford
Scheme: EPSRC Fellowship - NHFP
Starts: 18 June 2018 Ends: 17 June 2021 Value (£): 488,022
EPSRC Research Topic Classifications:
Bioinformatics
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology R&D
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 1 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
In the proposed research project I would build computational tools and analyses that help to improve the efficiency of drug discovery through enhanced analysis of protein-ligand interactions.

The continuing influx of genetic information has lead to an explosion in the number of putative macromolecular disease targets including proteins. Small molecules (<900 Da) can bind to and then modulate the activity of those protein targets. Small molecules can thus be used as drugs to treat diseases and as tools to reveal linkages between potential targets and disease. Tools and drugs must bind strongly to their protein of interest (potency). Most small molecule drugs and tools do so through non-covalent interactions such as hydrogen bonds, electrostatic and hydrophobic interactions (protein-ligand interactions). The quantitative understanding of such interactions remains poor and so the automated design of small molecules with optimised interactions is currently not possible. Current state of the art in small molecule optimisation involves multiple time-consuming and expensive cycles of subjective human-driven design, chemical synthesis and experimental testing. For each potent small molecule this typically takes years and costs millions of pounds, often ending in expensive failure.

A major reason for the lack of understanding of protein-ligand interactions and routes to optimising them is that high quality, systematic data has until now been the preserve of specialised industry groups (and very expensive to generate). The XChem collaboration between Diamond Light Source and the Structural Genomics Consortium (SGC) Oxford enables medium-throughput generation of such structural data for the first time. Over two years, XChem has generated thousands of high-quality 3D protein-ligand structures on more than 30 biomolecule targets. Crucially, it is now conceivable to generate systematic datasets (e.g. exploring the effect of small chemical alterations on binding and of the role of solvation) at atomistic resolution.

In this fellowship, I will build such a systematic dataset on five protein targets involving 100s of novel experimentally determined protein-ligand structures. I will do so by combining novel computational tools with the breakthrough XChem facility for high-throughput protein-ligand X-ray crystallography. Specifically, I will build on the small molecule Astex Graph Database that connects experimental XChem hits with easily synthesised molecules provided by vendors and collaborators. These connections will be used to design future experiments that explore protein-ligand binding systematically but in a feasible manner. I will then combine the experimental protein-ligand interaction data and computational energetics methods with the small molecule data in this Graph Database. Finally, I will use this comprehensive and connected Graph Database to design automated routes for compound optimisation using structural data.
Key Findings
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Potential use in non-academic contexts
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Summary
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Further Information:  
Organisation Website: http://www.ox.ac.uk