Interatomic Potentials for Small Molecule Radical Reactions

Lead Research Organisation: University of Cambridge
Department Name: Chemistry

Abstract

Year 1: Generic training activities for all first-year student members of the CDT.

Year 2-4: Machine-learned interatomic potentials are emerging tools in atomistic modelling. They bridge the gap between the accurate yet slow quantum-mechanical (QM) methods and computationally cheap, but not satisfyingly accurate classical force fields. Although they have been applied to modelling a range of materials and organic molecules, their use in modelling reactions is yet to be explored.

ML potentials are built on reference databases consisting of three-dimensional structures and energies and forces estimated with a highly accurate QM methods. As a result they are only applicable to structures not too dissimilar to ones the force fields were fitted to. Consequently, a lot of attention is devoted to composing the training data sets. A recent development is to use an imperfect potential to explore the chemical space and improve the potential with collected data. The challenge in our case is to determine how to similarly gather data for small organic molecules and radicals for the relatively constrained problem we are trying to solve.

We will narrow the scope of "modelling reactions of small molecules and radicals" by focusing on hydrogen abstraction reactions of methoxy radical and drug-like molecules, relevant to Cytochromes P450 metabolism. To model reactions, one would need to gather representative data for the equilibrium geometries of reactants and products as well as data sampled from the reaction path between them.

Planned Impact

Who might benefit from this research? How might they benefit from this research?

Students
(a) The major beneficiaries of the CDT will, of course, be the students that train on the program. They will be equipped with a set of skills that will be highly desirable in the organic molecule making industries. Although the proposal is directing towards a need in the pharmaceutical industry, the training and research skills are totally transferable to industries like the argochemical sector (this is an almost seamless transition as the nature of the needs are near identical to that of pharma) but also the fine chemicals industries, CRO's who serve all of these industries. With some adaptation of the skills accrued then the students will also be able to apply their knowledge to problems in the materials industries, like polymers, organic electronics and chemical biology.

(b) Synthesis will also be evolving in academia and students equipped with skills in digital molecular technologies will be at a significant advantage in being apply to implement the skills acquired while training on the CDT. These students could be the rising stars of academia in 10 years time.

(c) The non-research based training will benefit the students by providing a set of transferable skills that will see them thrive in any chosen career.

(d) The industry contacts that will be generated from the variety of interactions planned in the CDT will give students both experience and insight into the machinations of the industrial sector, helping them to gain a different training experience (form industry taught courses) and hands on experience in industrial laboratories.

(e) All student in UCAM will be able to benefit in some way form the CDT. Training courses will not be restricted to CDT students (only courses that require payment will be CDT only, and even then, we will endeavour to make additional places available for non-CDT students). The overall standard of training for all students wil be raised by a CDT, meaning that benefit will be realised across the students of the associated departments. In additional, non CDT students can also be inspired by the research of the CDT and can immerse new techniques into their own groups.

Academic researchers in related fields (PIs)
(a) new research knowledge that results from this program will benefit PIs in UCAM and across the academic community. All research will be pre-competitive, with any commercial interests managed by Cambridge Enterprise

(b) a change in mnidset of how synthetic research is carried out

(c) new collaborations will be generated withing UCAM, but also externally on a national and international level.

(d) better, more closely aligned, interactions with industry as a result of knowledge transfer

(e) access to outstanding students

Broader public
(a) in principle, more potential medicines could be made available by the research of this CDT.

Economy
(a) a new highly skilled workforce literate in disciplines essential to industry needs will be available
(b) higher productivity in industry, faster access to new medicines
(c) spin out opportunities will create jobs and will stimulate the economy
(d) automation will not remove the need for skilled people, it will allow the researchers to think of solutions to the problems we dont yet understand leading to us being able to discover solutions faster

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024220/1 01/06/2019 30/11/2027
2276986 Studentship EP/S024220/1 01/10/2019 30/09/2023 Elena Gelzinyte