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Artificial Intelligence for Drug Discovery MSc

Part of: Chemical Sciences and Computer and Data Science

Artificial Intelligence is revolutionising drug discovery, allowing us to develop better medicines faster.

This MSc will teach you essential concepts of drug discovery and artificial intelligence. Through intensive training, you'll develop the advanced computational skills required to apply AI techniques to drug discovery.

This programme is ideal for graduates in Chemistry, Pharmaceutical Chemistry, Medicinal Chemistry, Biochemistry or a related discipline looking to pursue a career in this rapidly growing field.

  • This is one of the first programmes in the world to focus on the applications of AI in Drug Discovery. 
  • You will develop advanced computational and programming skills with exposure to state-of-the-art applications and languages such as Python, TensorFlow, DeepChem and Alphafold. 
  • No prior knowledge of programming is assumed, making this ideal for graduates from Chemistry or a related discipline who would like to specialise in the applications of AI in Drug Discovery. 
  • Chemistry at Queen Mary is currently ranked 8th in the UK (REF 2021) for its research impact. You’ll learn from world-leading researchers in computational chemistry.

Study options

Starting in
September 2025
Location
Mile End
Fees
Home: £15,250
Overseas: £29,950
EU/EEA/Swiss students
Apply nowRegister your interest

What you'll study

The programme has been designed to prepare you for a career in computational drug discovery. 

You'll developed advanced computational skills. We will teach you how to code in Python and how to implement and use machine and deep learning methods in the context of drug discovery, interpret their predictions and assess their quality. 

You'll build a solid foundation in the essential concepts of drug discovery such as how to design a molecule to bind a target protein and how to optimise its chemical structure. 

You'll have the opportunity to apply your computational skills to drug discovery as you study scientific computing and learn modelling and simulation techniques for biomolecular systems. 

A full list of modules can be found below. 

Structure

  • 7 compulsory modules
  • Research project

 

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Compulsory/Core modules

The students work on research topics in one of the areas of Artificial Intelligence for drug discovery set by their project supervisors. Computational work is the principal component of the projects. The work also involves critical evaluation of previously published results. A dissertation is prepared.

This module is designed to teach students about the process of lead compound optimization in drug discovery. Lead compounds are compounds that show promising activity against a specific target, but often require further modification to improve their efficacy, safety, and pharmacokinetic properties. Students will learn how to fine-tune lead compounds through various chemical modifications to improve their potency, selectivity, pharmacokinetics, and toxicity profiles. The module will cover topics such as structure-activity relationships, chemical modifications, synthetic viability, ligand efficiency, bioisosteres, prodrugs and ADME/Tox profiling.

This module covers the main principles of in silico ligand-based approaches to drug discovery, with a programming component that builds upon the programming skills developed in CHE709. Topics include molecular representations, descriptors and fingerprints, molecular similarity, database searches, application of machine learning to QSAR and ADMET prediction. Tools for the critical assessment of method performance will also be presented.

The module covers advanced deep learning techniques applied to drug discovery. Topics include chemical datasets for machine learning benchmarking, deep learning for protein structure prediction, binding affinity prediction and virtual screening, and generative models for de novo drug design. Students will learn both how to use existing applications based on machine learning and how to develop deep learning pipelines in the context of drug discovery through hands-on computational sessions.

This module covers the main molecular modelling techniques used in drug discovery, with emphasis on structure-based approaches. Topics include protein structure, protein-ligand interactions, classical force fields, homology modelling, molecular docking, structure-based virtual screening and molecular dynamics simulations. Practical lab sessions will complement face-to-face teaching and provide the students with the opportunity to use a range of popular modelling tools for drug discovery and assess their performance.

The discovery and development of new drugs is critical for improving human health and treating a wide range of diseases. Medicinal chemistry plays a vital role in the drug discovery and development process by providing the fundamental knowledge and principles necessary to design and optimize drugs with improved efficacy and safety profiles. This module equips students with a comprehensive understanding of the principles and concepts of medicinal chemistry, including drug targets, drug-receptor interactions and pharmacology. Students will develop the skills necessary to design and optimize drugs with improved efficacy and safety profiles. By the end of the module, students will be able to critically evaluate the impact of medicinal chemistry on drug discovery.

This module covers key concepts of scientific programming including variables, data structures, control flow, regular expressions, functions and libraries for data analysis and visualisation. Use of coding to query chemical databases will also be introduced. Face-to-face teaching will be followed by practical sessions in the computer lab, where student will have the opportunity to build their coding skills and apply them to data analysis and visualisation in the context of drug discovery using an integrated development environment such as JupyterLab. The module does not assume any previous knowledge/experience of programming.

The module covers fundamental concepts of machine learning with emphasis on the development of practical skills required for the selection and application of machine learning methods to defined problems. Topics include data representation and preparation, unsupervised learning methods, regression and classification methods, artificial neural networks and performance evaluation. Face-to-face teaching will be combined with extensive hands-on sessions in the computational lab.

Assessment

  • 67% Modules
  • 33% Research project

Research project

You will complete an individual research project working alongside one of our world-leading researchers. Below is a list of sample research projects. Please note that these projects are not guaranteed and are subject to the availability of an appropriate supervisor.

- Reducing cardiac cytotoxicity with computational methods
- A combined machine learning and synthesis approach to the generation of new cannabinoid mimics with therapeutic potential
- Generative models for drug discovery in skeletal and cardiac muscle disease
- Ab initio machine learning forcefields to investigate water-protein interactions
- Machine learning models for photo-electrochemical reactions
- Evaluation of deep learning-based methods for the prediction of peptide-receptor interactions
- Machine learning and druggability prediction for target prioritisation
- Transfer learning for drug discovery

Teaching

You’ll be taught through a combination of lectures, tutorials and hands-on sessions in the computational lab. The Department of Chemistry at Queen Mary recently received a LearnSci Teaching Innovation Award for their commitment to new and exciting ways of teaching. 

The Department of Chemistry is currently ranked 8th in the UK for its research impact according to the 2021 REF results. You will be taught by - and conduct research with - world-leading academics and members of Queen Mary's Physical and Computational Chemistry, and Synthesis and Catalysis research groups. 

Where you'll learn

Facilities

About the School

School of Physical and Chemical Sciences

The School of Physical and Chemical Sciences (SPCS) is known for its world-leading research, and you’ll be learning from scientists at the forefront of their field. We’re also known for our outstanding teaching, which was recognised with a LearnSci Teaching Innovation award in 2021.

We’re a friendly, international and intellectually-curious community. And we’re looking forward to helping you thrive in your area of study and research.

Contact us

School of Physical and Chemical Sciences

Career paths

This programme is excellent preparation for a career in drug discovery and development. You will be well placed for roles such as:

  • Computational Drug Discovery Scientist
  • AI Research Scientist
  • Computational Chemist
  • Data Scientist

The computational skills developed can also be applied to a wide range of sectors and industries. 

Graduates will also be well-equipped for further research and may be interested in Queen Mary's AI for Drug Discovery Doctoral Training Programme. 

Fees and funding

Full-time study

September 2025 | 1 year

Conditional deposit

Home: Not applicable

Overseas: £2000
Information about deposits

Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award

Funding

There are a number of ways you can fund your postgraduate degree.

Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:

Entry requirements

UK

Degree requirements

A good 2:2 (55% or above) or above at undergraduate level in Chemistry, Pharmaceutical Chemistry, Medicinal Chemistry, Biochemistry, Pharmacy, Biomedical Sciences or a related discipline.

Find out more about how to apply for our postgraduate taught courses.

International

English language requirements

The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.

The English Language requirements for entry to postgraduate taught and research programmes in the School of Physical and Chemical Sciences falls within the following English band:

Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking

We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.

Visas and immigration

Find out how to apply for a student visa.

Contact

School of Physical and Chemical Sciences

Postgraduate Admissions

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