MSc Computer-Aided Drug Discovery for Cancer Therapeutics

Use the links below to view lists of courses in related subject areas.

• Pharmacy and Pharmaceutical Sciences

We require an honours degree (minimum Upper Second) or overseas equivalent:

• Medicinal chemistry
• Pharmacology
• Pharmacy
• Pharmaceutical sciences
• Chemistry
• Biochemistry
• Biochemical or chemical engineering

International students must demonstrate English proficiency through a secure and approved testing system. We ask for English language proof if you are from a non-majority English-speaking country.

English language students whose first language is not English require a minimum of one of the following:

• IELTS : 7.0 with a minimum of 6.5 in each component
• Internet-based TOEFL : 100 with a minimum of 25 in each component

Some English Language test results are only valid for two years. Your English Language test report must be valid on the start date of the course.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

For the latest scholarship and bursary information, please visit the fees and funding page .

Foundations in Chemistry and Biology (15 credits)

Learn enough about the background chemical and biological science to enable you to progress through the remainder of the modules, regardless of your previous scientific background. You will learn about foundational concepts in medicinal and computational chemistry, as well as key biological concepts and methods. The underlying biological mechanisms driving carcinogenesis and understanding of how oncogenic pathways can be exploited to generate anticancer therapeutics will be provided.

Basic Pharmacokinetics and Pharmacodynamics (15 credits)

This introductory unit is designed to give you an understanding of fundamental concepts in pharmacokinetics and pharmacodynamics: essentially how drugs get into the body, how they get around the body and how they get out of the body. Emphasis is given to explaining how chemical properties of drug interact with physiological aspects of the human body to affect the behaviour of different drugs and the variation between individual patients. Modelling and data analysis is described with reference to drug discovery, drug development and therapeutic usage. The module also provides experience in solving numerical problems relating to the time-course of drugs and their metabolites in the body.

Drug Discovery Part I (15 credits)

You will be introduced to the drug discovery process. You will learn and put into practice concepts relating to target identification, lead identification and optimisation, pre-clinical and clinical studies – considering both the models used in the development of new drugs and how by understanding the relationship between molecular structure and biological properties can be applied to design new drugs. You will learn how genomic approaches as well as bioinformatics resources and tools are used in the process of anticancer drug development.

Statistics for Health Data Science (15 credits)

Currently, there is a large amount of health and related data that is not analysed to provide insights into healthcare delivery. A core skill required of a health data scientist is analysis of various forms of health data; the unit will cover the fundamental knowledge required to do this, including understanding data; pre-processing steps; key analytical skills and a suitable statistical programming language. You will learn what can be achieved through the analysis of health data. Key research questions will be drawn from teaching staff and their networks to illustrate these techniques.

Drug Discovery Part II (15 credits)

You will focus on the computer-aided elements of drug discovery and optimisation. You will learn about well-established computational methods ranging from chemical informatics through force-field-based simulation to quantum mechanical methods. You will also learn from practitioners of the latest tools applying AI and machine learning to drug discovery and optimisation. You will learn the mechanisms of the development of small molecule drugs antigenicity and how this leads to drug resistance.

Research Methods (15 credits)

You will be introduced to the scientific method and essential ideas about how it is implemented in drug discovery and optimisation. You will learn essential skills for using the literature and scientific communication that will inform your project dissertation and presentation.

Established and Experimental Therapeutics (15 credits)

Explore the principles that underpin the rapidly expanding field of immuno-oncology and other targeted therapies. You will gain a solid understanding from our experts on the methodologies and how cutting-edge, translational research contributes directly to clinical decision making. Through increased understanding of this crucial partnership between research and clinical practise, you will also develop the skills and insights to explore ways to repurpose existing drugs to target new tumour types, and learn about novel approaches. You will gain critical insights into the types of targeted therapy and new medical products that use gene therapy, cell therapy and tissue engineering.

Computational Methods for Multi-Modal Data Analysis (15 credits)

The analysis of multi-modal data has significantly advanced our understanding of complex biological systems and their applications in healthcare. By integrating omics, imaging and health data using cutting-edge machine learning methods, researchers can infer relationships across diverge modalities. This unit equips you with fundamental knowledge and hands-on computational skills to analyse and interpret such multi-modal datasets effectively. Three main areas will be covered to give you the range of skills to be able to work with large scale, multi-modal data.

Dissertation (60 credits)

You will have an opportunity to work with the world-class researchers at the University to undertake a collaborative research project that will allow you to bring together and apply the methods and ideas that you have learned about throughout the course. This will develop skills such as multidisciplinarity and teamwork, which are common in drug discovery. You will present your findings in a report and as a presentation to your peers and researchers from the University.

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: dass@manchester.ac.uk