Data-driven realisation of molecular editing for drug discovery

Drug discovery pipelines are driven by iterative cycles in which molecules are designed, synthesised, purified and tested. A remarkably limited toolkit of reactions dominates discovery, which has contributed to the historic uneven exploration of chemical space, and has tended to focus attention on molecules with sub-optimal properties. Many reactions that would be potentially valuable for drug discovery have recently emerged that could complement this established reaction toolkit.<br /> <br /> The main hindrance in harnessing a broader reaction toolkit, such as molecular editing reactions, stems from insufficient knowledge of applicability across a range of substrates, and, thus, a low confidence in using these methods in a resource-pressured real-world context. Synthetic challenges can arise because bioactive molecules are typically more highly functionalised and relatively polar, and such substrates systematically perform less well in reactions that have been optimised using model (simple, commercially available) substrates. How, then, can the reaction toolkit be broadened to enable molecular editing reactions to be harnessed within drug discovery programmes?<br /> <br /> We propose to develop a data-driven approach to enable prediction of the success of molecular editing reactions. The specific reactions to be investigated will be chosen on the basis of potential strategic value for drug discovery e.g. skeletal editing reactions that enable a core ring system to be precisely and directly modified (vide infra). Initially, we will establish high-throughput methods to assemble training data by determination of reaction outcomes as a function of the substrates and conditions used. We will develop machine learning strategies to enable prediction of the outcome of reactions outside the training set. Finally, we will validate the approach by comparing the predicted and experimentally-determined outcomes of a range of molecular editing reactions involving substrates outside the training set. Overall, the resulting tools will enable uptake of molecular editing reactions within drug discovery.<br /> <br /> The project is collaborative with Exscientia, an AI-enabled drug discovery company.<br />

<p><strong>Objective 1. Establish high-throughput workflow to assemble a knowledge base of molecular editing reactions</strong><br /> Initially, we will configure selected reaction classes for implementation in an automatable high-throughput format. &nbsp;We will prioritise molecular editing reactions that enable direct interconversion of aromatic heterocycles. Arrays of reactions will be executed on ~300 &mu;l scale in 96-well plate format. &nbsp;The resulting crude reaction mixtures will be analysed by UPLC.</p> <p>The high-throughput workflow will be exploited to assemble training data for the prioritised reaction classes. &nbsp;For each reaction, a virtual set of potential substrates will be assembled by filtering a database of commercially-available compounds by structure and molecular properties. &nbsp;The virtual set will include both substituted analogues of known substrate classes and related heterocycles that are not (yet!) known to be substrates. Reaction arrays will be designed and executed that involve diverse combinations of substrates and reagents (and, where appropriate, conditions), and reaction outcomes determined. &nbsp;For selected productive reactions, we will purify (by mass-directed HPLC) and characterise the resulting products. &nbsp;This workflow will enable assembly of a knowledge base of molecular editing reactions that will be harnessed to predict the outcome of future discovery-relevant reactions.<br /> &nbsp;<br /> <strong>Objective 2. Develop machine learning strategies to prediction of the outcome of molecular editing reactions</strong><br /> We will implement machine learning workflows to facilitate efficient exploration of the space of possible reactions and study predictive performance on unseen substrates. If feasible, we will explore alternative featurisations of the input data, such as exposing suitable properties from quantum mechanics calculations to the yield prediction model.</p> <p><strong>Objective 3. Validate the overall approach for predicting the outcome of molecular editing reactions</strong><br /> We will determine the value of the developed machine learning tools to predict the outcome of specific molecular editing reactions. &nbsp;To showcase value in discovery, we will predict the viability of ~50 precise molecular editing reactions. &nbsp;These reactions will involve substrates with complexity and functionality that is typical of compounds investigated in drug discovery. &nbsp;Crucially, for both reactions that are predicted to be successful and unsuccessful, we will compare predicted and experimental reaction outcomes. &nbsp;The approach will enable us to identify limitations of, and to enable refinement of, the machine learning tools. &nbsp;Overall, we will validate our approach for predicting the outcome of precise molecular editing reactions.<br /> &nbsp;</p>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Chemistry FT,&nbsp;</strong></em>in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Data-driven realisation of molecular editing for drug discovery</strong></em> as well as&nbsp;<a href="https://eps.leeds.ac.uk/chemistry/staff/4180/professor-adam-nelson"><strong>Professor Adam Nelson</strong></a> as your proposed supervisor&nbsp;and in the finance section, please state clearly&nbsp;<em><strong>the funding source that you are applying for is EPSRC Case Competition Studentship 2024/25: Chemistry</strong></em></p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 6 May 2024:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p class="MsoNoSpacing">A highly competitive EPSRC CASE Competition Studentship in partnership with Exscentia, offering the award of full academic fees, together with a tax-free maintenance grant of &pound;19,237 and an additional Top-Up of &pound;3,300 per year for 3.5 years.&nbsp; Training and support will also be provided.<br /> <br /> This opportunity is open to all applicants. All candidates will be placed into the EPSRC CASE Competition Studentship Award Competition and selection is based on academic merit.<br /> <br /> <em><strong>Important:</strong></em>&nbsp; Any costs associated with your arrival at the University of Leeds to start your PhD including flights, immigration health surcharge/medical insurance and Visa costs are <em><strong>not</strong></em> covered under this studentship.</p> <p>Please refer to the&nbsp;<a href="https://www.ukcisa.org.uk/">UKCISA</a>&nbsp;website for&nbsp;information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about this project, please contact Professor Adam Nelson by email to&nbsp;<a href="mailto:A.S.Nelson@leeds.ac.uk">A.S.Nelson@leeds.ac.uk</a></p> <p>For further information about your application, please contact Doctoral College Admissions by email to <a href="mailto:maps.pgr.admissions@leeds.ac.uk">maps.pgr.admissions@leeds.ac.uk</a></p>