Thesis work: Automating molecular structure elucidation

Thesis work: Automating molecular structure elucidation using machine learning

Thesis type: 30 credits
Project start: January 2022
Student location: On Site, Gothenburg Sweden

Are you currently pursuing a master’s degree with a burning interest in solving real world problems? Are you interested in “leveling-up” you Machine Learning, data handling and processing skills? Then this master’s thesis project at AstraZeneca might be of interest to you!

The pharmaceutical industry has a strong focus on developing and synthesizing new drugs as quickly and efficiently as possible. Synthesis  is often performed using automated robots which can deliver libraries consisting of thousands of compounds. To confirm the successful synthesis of these molecules, several characterization techniques are used, among them Nuclear Magnetic Resonance (NMR). The analysis of this data is very time consuming and requires a lot of domain knowledge. In this project we aim to train a machine learning model to predict the structure of molecules based on their NMR spectra in order to distinguish intended structures from unitended byproducts automatically. 

AstraZeneca is a global, innovation-driven biopharmaceutical business that focuses on the discovery, development and commercialization of prescription medicines for some of the world’s most serious diseases. But we're more than one of the world's leading pharmaceutical companies. At AstraZeneca, we're proud to have a unique workplace culture that inspires innovation and collaboration. Here, employees are empowered to express diverse perspectives - and are made to feel valued, energized and rewarded for their ideas and creativity.
You will be working in a dynamic group of experts in various functional areas ranging from machine learningand data science to chemistry and spectroscopy, as well as with our academic partners form the University of Cambrdige. 

In this project, you will learn to manipulate and prepare spectral data as well as build and evaluate various machine learning models for molecular structure elucidation. You will be mentored by experts in data science and spectroscopy while you develop multimodal neural network architecture designs and test their performance. Your goal is to produce an automated approach that could distinguish between desired and undesired molecules using their NMR (and potentially other) spectral data. 

The work is to be conducted from March to August 2023, or as agreed with the student at the Gothenburg site of Astrazeneca in Sweden.

Essential Requirements 

Desirable Requirements 

2022-11-04, apply by sending in your resumé in English at randstad.se
For more information: Contact Malin Fridholm, [email protected]

If you’re inspired by the possibilities of science to make a difference and ready to discover what you can do – join us! 

At AstraZeneca we are guided in our work by a strong set of values, and we’re resetting expectations of what a bio-pharmaceutical company can be. By truly following the science, we pioneer new methods, new thinking and bring unexpected teams together. From scientists to sales, lab techs to legal, we’re on a mission to turn ideas into life-changing medicines that transform lives. We need great people who share our passion for science and have the drive and determination to meet the unmet needs of patients around the world. If you’re swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.

AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law. AstraZeneca only employs individuals with the right to work in the country/ies where the role is advertised.