The lead optimisation (LO) stage of the drug discovery process is an essential iterative process that optimises absorption, distribution, metabolism, excretion and toxicity properties. Traditionally, the LO stage is represented through Markush structures and structure-activity relationship tables. Together they characterise the core scaffold and the varying substituents and the variation within a property of interest. These representations are straightforward to interpret a large number of molecules. Unfortunately, due to the structure, it is difficult to compare cores that have a minor variation as a new representation is generated.<br/>To overcome this issue, a new visualisation has been designed, using a combination of reduced graphs (RGs) and substructural fragments. The use of RGs encourages more flexibility that focuses on intermolecular interactions. RG cores are extracted from the dataset these are comparable to the Markush structures, but instead of using chemical graphs RGs are used. The nodes of a RG are substructural fragments from molecules that contain that core. Therefore, the visualisation focuses on the interactive nature of the molecules as well as the underlying chemical structures. Due to the emphasis on both of these features it allows a chemist to determine whether a specific substructural fragment is of importance to the activity or whether any fragment that can form a given interaction is sufficient.<br/>Along with the visualisation, chemists are provided with more details when considering potential new molecules to synthesis. New molecules that are either suggested from de novo design algorithms or the chemist themselves, can then be mapped onto the RG cores and scored. Two scores have been constructed. The first explains the level of exploration, which reflects the chemical space being added. The second describes the level of exploitation that this molecule demonstrates in respect to the dataset and how it improves the information already learnt.

Using reduced graphs to cultivate lead optimisation series

ACS Spring Meeting 2021

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