Data-driven approaches for fragment merging

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This project focuses on fragment-based drug discovery (FBDD), involving the screening of low-molecular-weight compounds against a target of interest to find chemical starting points that can be optimized to become lead-like molecules. The proposed work is situated within the lead optimization stage of the FBDD pipeline, which looks at how crystallographic fragment hits and the structural information they yield can be exploited to propose larger, drug-like compounds that bind to a target with increased affinity. There are several approaches to fragment elaboration, including fragment growing, linking and merging. Fragment merging is a relatively unexplored technique compared with its counterparts yet has the potential to find potent molecules. Existing strategies for merge design include manual design by a medicinal chemist, which is slow and not scalable to large datasets, and de novo design, which often results in molecules that lack synthetic accessibility and are therefore difficult and costly to pursue. Thus, the aim of this project is to use knowledge-based approaches to propose fragment merges that are synthetically feasible, allowing rapid and cheap progression from fragment hits to lead-like compounds. Various data-driven approaches will be explored, including database exploitation and AI-based techniques, which may be used synergistically to propose new compounds. Use of the former has already been demonstrated during the rotation project, which involved use of the Fragment Network, a graph database containing catalogue compounds, to create a pipeline able to find and filter fragment merges that can be prioritized for further screening. Several avenues to extend this work by enhancing the efficiency of this tool and increasing the diversity in the molecules found have already been identified. Compounds proposed by this project will also have the opportunity for experimental validation. Automated synthesis planning and execution on a robotic platform are currently being explored at XChem, and a key component of this work will be identifying compounds that can be made given the available synthetic repertoire. Integrating this entire pipeline has the potential to be high impact with respect to improving the speed at which we can progress potent ligands in drug development, thereby reducing the number of iterations required for the design-make-test cycle. The proposed work (which will exist in conjunction with another DPhil project, focused on the robotics aspect of the pipeline) will involve industrial collaboration with Vernalis and LifeArc; exact industrial supervisors are to be confirmed. This project falls within the EPSRC's 'Computational and theoretical chemistry' research area and ties strongly to the council's outlined strategies within this field. This research is highly interdisciplinary and will involve collaboration with beamline scientists, medicinal chemists and automation experts. As described above, the software developed during this project will have direct, actionable consequences for the drug discovery community, producing molecules that can be purchased or easily synthesized for further screening. Making source code and data from this project available will allow others to apply this technique to new targets and enable comparison with their own developed algorithms.