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Chemistry, Pharmacology and Drug Discovery at UEA
Find out more about studying Chemistry, Pharmacology and Drug Discovery at UEA, and browse our other courses.
Find out moreMSc Data Science for Drug Discovery
Key Details
- Attendance
- Full Time
- Award
- Degree of Master of Science
- Course Length
- 1 year
- Course Start Date
- September 2026
Other Start Dates
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Why you should choose us
Course Overview
The future of drug discovery is data-driven, and this MSc places you at the intersection of computational innovation and biomedical science. As pharmaceutical research increasingly relies on AI, machine learning, and predictive modelling to accelerate the development of new medicines, employers are seeking scientists who can integrate digital tools with deep biological and chemical insight. This programme equips you with exactly those capabilities.
At UEA, you’ll gain advanced training in data science, cheminformatics, bioinformatics and AI‑assisted molecular design, learning how to analyse complex chemical and biological datasets that underpin modern drug discovery. You’ll develop fluency in Python, statistics, machine learning, and data visualisation, whilst also building the ability to model molecular systems, predict properties, and identify promising drug targets in silico before they reach the lab.
Drawing on the world-leading expertise of the Norwich Research Park, you’ll explore every stage of the drug discovery pipeline—from target identification and hit discovery, through optimisation, to understanding how medicines progress towards the market. Through interactive teaching, hands-on computational work, laboratory experience, and case-study-driven learning, you’ll develop both technical depth and practical insight.
Your research project, supervised by active researchers at the forefront of digital chemistry, pharmacology and biomedical science, gives you the opportunity to apply your computational and analytical skills to real scientific challenges.
You’ll graduate with a rare and highly sought-after combination of digital, computational, and drug discovery expertise, ideal preparation for careers in pharmaceutical R&D, AI‑enabled biotechnology, data‑driven research roles, or progression to PhD study.
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Study and Modules
Structure
Our MSc in Data Science for Drug Discovery is a full‑time, one‑year programme that blends advanced data science training with deep insight into the modern drug discovery pipeline. Designed for scientists eager to work at the interface of computation, chemistry and bioscience, the course equips you with both the theoretical understanding and hands‑on skills needed to tackle real‑world challenges in pharmaceutical research.
You begin by building a strong digital foundation through modules that develop core competencies in programming, handling large datasets, extracting meaningful patterns, and managing complex scientific projects. Alongside this, modules train you to communicate scientific insights clearly and effectively, preparing you for collaborative work across multidisciplinary teams.
As the course progresses, you can deepen your scientific focus through specialised modules tailored to introduce you to AI‑assisted molecular design, target identification, biomolecular analysis and predictive modelling. With cutting‑edge case studies and research-led teaching, we help you understand each stage of the discovery pipeline, from early-stage hit-finding to preclinical considerations. You'll also broaden your scientific perspective, exploring novel materials, nanoscale systems, and physical principles that increasingly inform therapeutic innovation.
A major component of the MSc is the independent research project, conducted over several months under the supervision of an expert researcher. This project allows you to apply computational, analytical and, where relevant, laboratory skills to a real drug discovery problem, developing your independence, scientific creativity, and professional confidence.
By the end of the programme, you'll have built a powerful blend of digital, analytical and drug discovery expertise, preparing you for PhD study or data‑driven roles in pharmaceutical R&D, biotechnology or academic research.
Compulsory Modules
Optional A Modules
(Credits: 20)Important Information
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, the University will endeavour to consult with students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will inform students.
Teaching and Learning
Teaching is through structured instruction with extensive hands-on practice to help you build confidence in both computational and scientific techniques. You can typically expect around 15 hours of weekly contact time through a blend of lectures, seminars, interactive workshops, and practical computing sessions. Lectures introduce core concepts in data science, cheminformatics, bioinformatics and drug discovery, while seminars and workshops give you the opportunity to apply these ideas to real datasets, research case studies, and current challenges in pharmaceutical development.
Computational labs also form a central part of your training, where you'll work directly with Python, datamining tools, molecular modelling platforms and visualisation software to analyse biological and chemical data. These sessions are supported by academic staff actively engaged in research, ensuring that your learning reflects the latest developments in the field.
Alongside structured teaching, you'll spend approximately 25 hours per week on independent study. This includes reading scientific literature, practising coding techniques, exploring datasets, and preparing for assessments. Throughout the course, you’re encouraged to develop your critical thinking, problem-solving and scientific communication skills.
A substantial component of your learning is the independent research project, during which you'll work closely with a supervisor to investigate a data driven drug discovery problem. This experience allows you to integrate your computational and scientific training while developing autonomy as a researcher.
Assessment
We’ll assess your learning in a combination of different ways depending on the module and learning objectives. These include formal exams, coursework assignments, and presentations.
A key feature of the MSc is the dissertation in which you’ll work closely with faculty to conceptualise, design and deliver an independent scientific research project.
Key to the project is the development of skills in applying data science techniques to real world questions. Your dissertation is assessed through a project proposal, dissertation report and presentation.
Employability
After the Course
Graduates from the MSc in Data Science for Drug Discovery are ideally positioned for careers at the forefront of modern pharmaceutical and biotechnology research. You’ll be equipped for roles in AI driven drug discovery, datacentric R&D, bioinformatics, cheminformatics, and computational modelling within industry or research institutes. The programme also provides a strong foundation for progressing to a PhD in areas such as computational chemistry, molecular data science, biomedical AI or pharmaceutical sciences. With highly transferable data skills, you could also pursue opportunities in broader analytics, healthcare technology, and emerging digital health sectors.
Careers
Examples of careers that you could enter include:
- AI Driven Drug Discovery and Design Driven Drug Discovery
- Bioinformatics and Computational Biology
- Cheminformatics and Molecular Modelling
- Pharmaceutical and Biotechnology R&D
- Healthcare Data Science and Digital Health
- Academic or Industrial Research
Discover more on our Careers webpages.
Entry Requirements
- This course is open to
UK and International fee-paying students. Choose UK or International above to see relevant information. The entry point is in September each year.
- Typical UK Entry Requirements
Degree classification
Bachelors degree - 2.2
Degree Subject
Pharmacology, Chemistry (organic or medicinal), pharmacy or related subjects (including Biochemistry and Biomedical Science)
- Admissions Policy
Our Admissions Policy applies to the admissions of all postgraduate applicants.
- This course is open to
UK and International fee-paying students. Choose UK or International above to see relevant information. The entry point is in September each year.
- Typical International Entry Requirements
Degree classification
Bachelors degree - 2.2 or equivalent
Degree Subject
Pharmacology, Chemistry (organic or medicinal), pharmacy or related subjects (including Biochemistry and Biomedical Science)
- English Foreign Language
Applications from students whose first language is not English are welcome. We require evidence of proficiency in English (including writing, speaking, listening and reading):
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IELTS: 6.0 overall (minimum 5.5 in all components)
Test dates should be within 2 years of the course start date.
We also accept a number of other English language tests. Review our English Language Equivalencies for a list of qualifications that we may accept to meet this requirement.
If you do not meet the English language requirements for this course, our UEA International Study Centre offers a variety of English language programmes which are designed to help you develop the required English skills.
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- Admissions Policy
Our Admissions Policy applies to the admissions of all postgraduate applicants.
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Fees and Funding
Tuition fees for the Academic Year 2026/27 are:
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UK Students: £12,350
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International Students: £25,700
We estimate living expenses at £1,171 per month.
Further Information on tuition fees can be found here.
Scholarships and Bursaries
The University of East Anglia offers a range of Scholarships; please click the link for eligibility, details of how to apply and closing dates.
Course Related Costs
Please see Additional Course Fees for details of course-related costs.
How to Apply
How to apply
Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.
To apply please use our online application form.
Further information
If you would like to discuss your individual circumstances prior to applying, please do contact us:
Postgraduate Admissions Office
Tel: +44 (0)1603 591515
Email: admissions@uea.ac.uk
International candidates are also encouraged to access the International Students section of our website.