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

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 data
Fragment-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.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.