A Hybrid Statistical/Mechanistic Approach to Predicting Reaction Conditions

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Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: The project will work towards building a statistical/mechanistic hybrid model to predict the conditions necessary for C-H activation of pyridines in the ortho-position to the nitrogen-atom, alongside two related challenges: defining the molecular context of a reaction and how to collaborate with others using the Unified Data Model (UDM). Molecular context can be defined using six unique parameter headers: information, energy, time, space, structure and substance. To better understand how to incorporate the structure of relevant compounds in a model, a review of molecular representation will be conducted. Representation of molecules was thought solved, however advancements in Machine Learning (ML) have brought about a resurgence of interest, particularly for the generation of novel compounds. Furthermore, a case study will show how UDM can be used to represent the data necessary for ML driven solvent selection. UDM is an XML-based data format capable of representing most classes of data relevant to chemistry. Building better prediction models for reactivity can aid in retrosynthesis, thus helping chemists in the lab, and is also an enabling factor for building autonomous synthesis platforms. There is precedent for the use of highly accurate reactivity models which are only valid in a small area of chemical space, and developing the concept of 'molecular context' can ease the meshing of such reactivity models.