Learning Chemical Representations and Retrosynthetic Agents to aid Closed Loop Drug Discovery

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The study will focus on the development of algorithms aiding closed-loop drug discovery enabled by a robotic scientist. A focus will be placed on learning improved, interpretable chemical representations and synthetically feasible de-novo molecular design from a pharmacophore. Learning optimal chemical representations for given tasks will be inspired by mathematical formalisations of Occam's razor. These concepts span algorithmic complexity and analogous decidable, albeit weaker versions such as the minimum message length (MML), minimum description length (MDL) principle, and regular and context-free languages. The problem of determining the optimal way to represent n compounds will also be investigated. Initial studies will consider solutions to the shortest grammar problem as a candidate representation. Solutions to this problem over a corpus of compounds deemed representative of chemistry may produce improved functional groups. Extensions of these ideas to the Turing decidable languages will be investigated. Improved chemical representations would positively affect all aspects of chemistry. New functional groups could serve to further explain chemical properties, behaviour and therapeutic effect. The shortest grammar which describes a set of drug molecules would likely capture those patterns important for drug activity, assuming such patterns are recognisable within a context free language.Design of models with knowledge of chemical synthesis will require logical agents. These models will be improved by good chemical representations in the synthetic process. Inductive logic programming will be explored in determining a set of valid rules required for chemical synthesis. A focus will be placed upon the molecular representation used. This representation will account for molecular context. Retrosynthetic tree search and reinforcement learning will be explored.Development of such algorithms would aid the speed of chemical and pharmaceutical discovery. Expectations for the project include the development of alternative chemical representations in line with Occam's Razor which account for molecular context, generative models which construct potential drug molecules from a pharmacophore and knowledge based retrosynthetic models.