Data-Driven Lead Optimisation for Drug Discovery

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Research questions:This research provides a lead optimisation tool that allows chemists to explore the chemical space that has previously been worked in for a certain target. This tool should then be able to suggest molecules or areas of chemical space that should be explored with reasoning. This should allow the chemist to have a greater understanding as to why a certain molecule has been selected to explore for a certain target. The main molecular representation that will be used for the tool is reduced graphs, which is a graphical representation that has been reduced down to the key interacting nodes. The visualisation tool should be an interactive interface that the chemists can have an overall view of the chemical space whilst also still allowing them to complex details of each molecule.Original methodology:The methodology that has been done so far has been to reduce the chemical structures down into reduced graphs. These reduced graphs have been made so they are customisable for the user as they can set different parameters and different definitions depending on what they are looking for and what they find key. The next step was to then find the maximum common substructure (MCS), both the connected and disconnected versions, using a python module RDKit. These MCS' are found so that some clustering techniques can occur as they are based upon similarity or dissimilarity scores which are found using the MCS in the Tanimoto coefficient equation. Several different clustering techniques are then performed and cluster validity techniques are applied in order to establish the best clusters for that dataset. A core reduced graph of these clusters are found to aid the visualisation technique. A visualisation is then produced that summaries this information, the chemical space that has been worked in. From here a method needs to be established that enables a reduced graph to be converted back into a chemical graph. This chemical graph will be ran in an activity model to see how well this molecule should perform and if the new prediction of activity is adequate then the molecule will be put forward as a suggestion. The visualisation will then be used to back up this suggestion.