At a Glance
- Tasks: Develop data-driven methods for molecular editing in drug discovery.
- Company: Collaborative project with Exscientia, a leading AI-enabled drug discovery company.
- Benefits: Tax-free maintenance grant of £19,237 plus an additional £3,300 per year for 3.5 years.
- Other info: Receive training and support while working on innovative solutions in a dynamic environment.
- Why this job: Join a cutting-edge research project that combines synthetic chemistry and AI to revolutionise drug discovery.
- Qualifications: First or Upper Second Class UK Bachelor (Honours) in Synthetic Chemistry, AI, or Machine Learning.
The predicted salary is between 19237 - 22537 £ per year.
Funding EPSRC CASE Competition Studentship in partnership with Exscentia Ltd, offering the award of fees, together with a tax‑free maintenance grant of £19,237 and an additional Top‑Up of £3,300 per year for 3.5 years. Training and support will also be provided.
Project summary: 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. 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?
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. 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.
The project is collaborative with Exscientia, an AI‑enabled drug discovery company.
Entry requirements: First or Upper Second Class UK Bachelor (Honours) or equivalent.
Subject Area: Synthetic Chemistry, AI and Machine Learning.
PhD Studentship: Data-Driven Realisation of Molecular Editing for Drug Discovery in Leeds employer: University of Leeds
As a PhD student at the forefront of drug discovery in collaboration with Exscientia Ltd, you will benefit from a supportive and innovative work culture that prioritises research excellence and professional development. With a generous funding package, including a tax-free maintenance grant and additional top-up, this studentship offers a unique opportunity to engage in cutting-edge research while receiving comprehensive training and mentorship from leading experts in synthetic chemistry and AI. Located in a vibrant academic environment, this role not only fosters personal growth but also contributes to meaningful advancements in healthcare.
StudySmarter Expert Advice🤫
We think this is how you could land PhD Studentship: Data-Driven Realisation of Molecular Editing for Drug Discovery in Leeds
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of synthetic chemistry and AI. Attend relevant events or webinars, and don’t be shy about introducing yourself. You never know who might have a lead on a PhD opportunity!
✨Tip Number 2
Prepare for interviews by brushing up on your knowledge of molecular editing and drug discovery. Be ready to discuss how your background aligns with the project’s goals. Show them you’re not just passionate but also knowledgeable!
✨Tip Number 3
Follow up after interviews! A quick thank-you email can go a long way. It shows your enthusiasm for the position and keeps you fresh in their minds. Plus, it’s a great chance to reiterate why you’re the perfect fit.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got all the resources you need to make your application stand out. Plus, it’s the best way to ensure your application gets seen by the right people.
We think you need these skills to ace PhD Studentship: Data-Driven Realisation of Molecular Editing for Drug Discovery in Leeds
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your application to highlight how your skills and experiences align with the project. We want to see your passion for synthetic chemistry and AI, so don’t hold back on showcasing relevant projects or coursework!
Be Clear and Concise:Keep your writing straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Remember, we’re looking for your ability to communicate complex ideas simply, especially in a data-driven context.
Show Your Enthusiasm:Let your excitement for the project shine through! We love candidates who are genuinely interested in drug discovery and molecular editing. A bit of personality can go a long way in making your application stand out.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way to ensure it gets into the right hands. Plus, you’ll find all the details you need about the studentship there.
How to prepare for a job interview at University of Leeds
✨Know Your Stuff
Make sure you have a solid understanding of synthetic chemistry, AI, and machine learning. Brush up on recent advancements in molecular editing and drug discovery. Being able to discuss these topics confidently will show your passion and expertise.
✨Research the Supervisors
Familiarise yourself with Professor Adam Nelson and Professor Steve Marsden's work. Knowing their research interests and recent publications can help you tailor your answers and demonstrate genuine interest in the project and their guidance.
✨Prepare Thoughtful Questions
Think of insightful questions to ask during the interview. This could be about the specific methodologies they plan to use or how they envision the collaboration with Exscientia. It shows you're engaged and thinking critically about the role.
✨Show Your Problem-Solving Skills
Be ready to discuss how you would approach challenges in drug discovery, especially regarding molecular editing reactions. Use examples from your past experiences to illustrate your problem-solving abilities and adaptability in a research setting.
