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Machine Learning and its Application in the Development of Novel Cannabinoids

School of Biological & Chemical Sciences

London United Kingdom Data Analysis Organic Chemistry Computer Science Pharmaceutical Chemistry Synthetic Chemistry

About the Project

Keywords: Data-centric engineering, Machine Learning, Novel Cannabinoids, Drug Discovery, Health

About the Project

The following doctoral studentship is available in the School of Biological & Chemical Sciences to start in September 2021. For more information on how to apply for this please visit the Data Centric Engineering CDT website.

Our first application deadline is 10th May (23:59 UK time). We encourage applicants to meet our first deadline. All applications received by 10th May will be considered on an equal footing, after the first deadline has passed.

We acknowledge that these are challenging times, and that applicants may need some extra time to put together documents and submit their application. We will therefore accept “late” applications on a rolling basis (until all places are filled) up until our second deadline: 31st May 2021 (23:59 UK time). Applications received after 10th May will be considered on a first come, first serve basis

Summary: A key step in the established drug discovery process is to generate a pool of suitable candidates for synthesis and subsequent biological screening, based on small structural modifications of an existing molecule of known biological activity, with the intent of improving potency, selectivity and minimising toxicity. Unfortunately, this requires substantial time, labour and financial resources. Molecular Dynamics and Monte Carlo Computer simulation techniques have greatly assisted the selection process owing to their ability to predict the binding affinity of a molecule with a given protein. In many cases, however, there is a ‘computational cost’ owing to the processing time and computational power required to obtain accurate calculations. A counterapproach is the use of knowledge-based and machine learning applications. Machine learning has been shown to be capable of yielding rapid and accurate predictions and has become orders of magnitude faster than traditional computational chemistry methods. Herein we propose to apply this emerging and potentially transformative method to the identification, targeting and subsequent synthesis of viable cannabimimetics, a class of compounds that have widespread health applications but have yet to be explored by such techniques.


Candidates should usually have an undergraduate degree of 2:1 or above in a relevant subject such as Engineering, Physics, or Computer Science, with at least at least three years relevant full-time employment (or equivalent) prior to applying to the programme. Through the application process, applicants should demonstrate relevant work-based competencies.

 We welcome applicants who have taken a career break.

Candidates without a relevant degree or qualifications below a 2:1 degree can be considered on a case-by-case basis should they have at least five years of relevant work experience post-degree, and through the application process can demonstrate work-based competencies. Depending on the academic study, we may look for at least 10 years of relevant work experience. 

Applicants must: 

  • have been awarded their most recent degree (if any) before September 2018 
  • have no restrictions on how long they can stay in the UK 
  • have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For example: 
  1. UK nationals
  2. EU nationals seeking permanent settlement in the UK
  3. nationals of any other country with unrestricted UK immigration status (e.g. indefinite leave to remain) and full-time residence in the UK at the time of application. 

If in doubt please contact us at with full details of your degree award dates, nationality, immigration status, and residency since 2018. 

Funding Notes

This studentship is funded by the EPSRC Centre for Doctoral Training (CDT) in Data Centric Engineering. It will cover a £20,500 stipend funding per year, tax free (equivalent to £25,000 gross salary). Tuition fees are also fully covered.

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