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1 Year On Campus Masters Program
Queen Mary University of LondonThe MSc Artificial Intelligence for Drug Discovery at QMUL trains students to apply machine learning, scientific programming and computational modelling to the design and prediction of new medicines. It suits graduates in Chemistry, Biochemistry, Medicinal Chemistry and related fields who want to enter computational drug-discovery roles.
Curriculum Structure:
Students begin with Scientific Programming for Drug Discovery and Fundamentals of Medicinal Chemistry, learning Python, data handling and the chemical principles behind drug action. They then progress to Machine and Deep Learning, Computational Ligand-based Drug Discovery, and Molecular Modelling for Drug Discovery, where they develop skills in QSAR modelling, virtual screening, docking and deep-learning-based prediction of molecular activity. The programme concludes with an Independent Project, allowing students to design and test a computational drug-discovery workflow using AI and modelling tools.
Focus areas: “Computational Drug Discovery, Machine Learning, Molecular Modelling, Ligand-based Design, Scientific Programming”
Learning outcomes: “Program and analyse chemical data; apply ML to molecular prediction; run modelling and virtual-screening workflows; conduct independent drug-discovery research.”
Professional alignment (accreditation): Designed to meet the skill needs of pharmaceutical, biotech and computational-chemistry sectors.
Reputation (employability rankings): QMUL’s strong research profile in chemistry and computational science supports excellent prospects for graduates entering AI-driven pharmaceutical research.
Students gain practical skills by applying AI and machine learning to real-world drug discovery challenges, using QMUL's computational infrastructure and working with biomedical datasets. This applied learning is central to the curriculum:
Software: Training in Python with key AI and cheminformatics libraries (TensorFlow, Scikit-learn, RDKit).
Computing Facilities: Access to QMUL's High-Performance Computing (HPC) resources.
Drug Discovery Projects: Hands-on work with chemical and biological datasets.
Research Project: An individual dissertation in AI-driven drug discovery.
Industry Context: Links to the Pharmaceutical Industry through research collaborations.
Interdisciplinary Focus: Combins AI with biomedical sciences for practical applications.
Graduates of Queen Mary University of London's MSc Artificial Intelligence for Drug Discovery master machine/deep learning, molecular modelling, Python/TensorFlow, and computational chemistry to accelerate pharmaceutical innovations and develop better medicines faster. Intensive training with real-world projects from world-leading computational chemists prepares alumni for high-impact roles at the AI-pharma intersection. Typical job roles: Computational Drug Discovery Scientist, AI Research Scientist, Computational Chemist, Data Scientist.
University services: Careers service offers CV support, interview training, industry networking for pharma/tech placements.
Employment stats/salary: High demand in biotech/pharma; competitive salaries in drug discovery/AI sectors.
University–industry partnerships: Collaborations with computational chemistry leaders; real-world drug projects.
Long-term accreditation value: REF 2021 top-10 research impact ensures globally recognized AI-drug expertise.
Graduation outcomes: Roles in pharma research, biotech, healthcare analytics worldwide.
Further Academic Progression: Pursue PhD in AI for drug discovery at Queen Mary via their Doctoral Training Programme.
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.USA- Bachelor's degree from a recognised institution with a minimum GPA 2.5 out of 4.0India - Bachelor Degree (minimum 3 years) from selected institutions .with minimum 50% to 60%IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and SpeakingTOEFL minimum score 92 overall with 21 in Writing, 19 in Reading, 18 in Listening and 21 in SpeakingAI Drug Discovery ScientistComputational ChemistPharmaceutical Data ScientistCheminformatics SpecialistBioinformatics ScientistMedical AI ResearcherDrug Design AnalystPharmaceutical AI Consultant
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