Much smarter and faster ligand discovery: Iterative, rational optimization of screening and follow-up libraries for the XChem fragment approach

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Much smarter and faster ligand discovery: Iterative, rational optimization of screening and follow-up libraries for the XChem fragment approach with deep learning from 3D structural dataFragment-based drug design (FBDD) is now well-established as a powerful approach to early-stage drug discovery, and has a track record of success for difficult targets where other methods have failed. This is due to the higher likelihood that a fragment (~third size of drug) will bind to the target, with more efficient interactions compared to a drug-sized ligand. These weakly binding fragments are then linked together to create a potent drug.Fragment-based approaches to ligand development, though well-established and comparatively powerful in experienced organisations, have yet to achieve their true transformative potential of making bespoke and potent ligands widely accessible cheaply (<£10k) and quickly (weeks). Even very significant recent public and commercial investments aimed at widening access, including Diamond's XChem facility or Enamine's REAL cheaply available compounds, have not fundamentally changed the game: developing a potent ligand still costs ~£0.5m. The focus of this project is to get much smarter at the very outset of the experiment: to figure out how to ensure the starting compounds are as likely as possible to yield all the information necessary to progress rapidly to potency, with as little experimental work as possible. The ambition is not new, but what is new are a vast trove (5 years' worth) of XChem data (already >150 experiments), and new Deep Learning approaches to understanding protein-ligand interactions, with new descriptions of synthetic space also coming into view.By the end of the full DPhil project, we envisage achieving (a) a far better general set of screening compounds; (b) an approach for selecting the optimal set of screening compounds for any given target, even if no ligand-bound structure has been solved previously; (c) algorithms to select the best next set of compounds, to allow effective iterations from very small initial screens; and (d) allow bypassing the screening step entirely for well-studied classes of protein. This project is based equally in the Department of Statistics and at Diamond Light Source, and falls within the EPSRC Biological Informatics research area.