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Predicting Reactivity and Selectivity with Machine Learning and AI

PGR-P-395

Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Competition funded
Source of funding
Other
Supervisors
Dr Bao Nguyen
Schools
School of Chemistry
<h2 class="heading hide-accessible">Summary</h2>

In this project, the student will develop a Machine Learning approach, in combination with molecular modelling, to predict reactivities of different reactive sites in a starting material against common reagents. While some experimental reactivity scales have previously been developed, the project will focus on extending them to predictions for novel compounds in silico.

<h2 class="heading hide-accessible">Full description</h2>

<p>Predicting reaction outcome and selectivity is part of the fundamental training of an organic chemist. However, real syntheses often still result in unexpected outcomes when complex molecules with multiple functional groups are involved. Synthetic chemists, with the assistance of modern computational chemistry, can usually explain these observations after the fact. However, predicting them before the experiments remain a critical challenge in the progress of synthetic chemistry toward a fully matured science.</p> <p>In this project, the student will develop a Machine Learning approach, in combination with molecular modelling, to predict reactivities of different reactive sites in a starting material against common reagents. While some experimental reactivity scales have previously been developed,[1] the project will focus on extending them to predictions for novel compounds <em>in silico</em>. This project will deliver:</p> <ul> <li>Computational approaches to quantify electronic and steric properties of a reactive site.&nbsp;</li> <li>Curated databases of reactivity and selectivity in reactions between organic substrates and common reagents.</li> <li>Interpretable Machine Learning models to predict reactivity using the curated databases.&nbsp;</li> </ul> <p>The toolkit will be demonstrated in case studies, including API-relevant synthetic steps, in collaboration with industrial partners in High Value Chemical Manufacture. The student will benefit from the unique expertise of Nguyen group in Physical Organic Chemistry, Computational Chemistry and machine learning in Chemistry.</p> <p>The project is best suited to a student with strong background and interest in organic mechanisms and synthetic chemistry. Prior knowledge of computational chemistry and machine learning is not necessarily required, as training will be provided for these important transferable skills. The student will also benefit from interdisciplinary training and seminar programmes in process chemistry as a member of the <a href="https://www.iprd.leeds.ac.uk/">Institute of Process Research &amp; Development, Leeds</a>.</p> <h5>References</h5> <p>[1] Herbert Mayr&rsquo;s reactivity parameters: <a href="https://www.cup.lmu.de/oc/mayr/reaktionsdatenbank/">https://www.cup.lmu.de/oc/mayr/reaktionsdatenbank/</a>.&nbsp;<br /> &nbsp;</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://eps.leeds.ac.uk/chemistry-research-degrees/doc/apply">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &lsquo;Predicting Reactivity and Selectivity with Machine Learning and AI&rsquo; as well as&nbsp;<a href="mailto:https://eps.leeds.ac.uk/chemistry/staff/4205/dr-bao-nguyen">Dr Bao Nguyen</a> as your proposed supervisor.</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>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

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.

<h2 class="heading heading--sm">English language requirements</h2>

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.

<h2 class="heading">Contact details</h2>

<p>For further information about the application procedure, please contact Doctoral College Admissions by&nbsp;email:&nbsp;<a href="mailto:maps.admissions.pgr@leeds.ac.uk">maps.admissions.pgr@leeds.ac.uk</a>&nbsp;or telephone: + 44 (0) 113 343 5057.&nbsp;</p> <p>For information regarding the project, please contact Dr Bao Nguyen by&nbsp;email:&nbsp;<a href="mailto:EMAIL@leeds.ac.uk">b.nguyen@leeds.ac.uk</a>, or telephone: +44 (0)113 343 0109.</p>