Oxford AI in Drug Discovery and Medicine Online Education Programme
The six-week Oxford AI in Drug Discovery and Medicine online programme takes a highly practical approach to teaching participants how to responsibly conduct AI-augmented drug discovery and get safe, life-changing medicines to market faster.
By exploring how the algorithmic analysis of clinical data can accelerate key phases in the drug R&D pipeline – including selecting and validating targets, identifying biomarkers and stratifying patient populations – the programme prepares you to predict therapeutic outcomes with greater speed and accuracy than traditional technologies and approaches.
Designed by the Nuffield Department of Medicine (NDM), one of the world's leading centres for biomedical and clinical research and co-founders of OpenBind, the programme leverages the experience of world-leading research teams to equip R&D professionals and clinician-researchers with highly applicable AI skills. Over the course of six weeks, you'll attend two live demonstrations of AI R&D tools, and develop a portfolio of practical deliverables that can be presented to an R&D committee, senior leadership or prospective investors.
INTRODUCTION TO THE COURSE
This AI in drug discovery course is designed to develop practical skills in:
- AI-augmented target selection and validation
- AI-powered therapeutics design
- Using AI responsibly for personalised medicine
- Designing in silico trials that connect therapeutics to patients
The programme provides various mechanisms that support your development as a researcher with practical AI skills, including:
- Live demonstrations of how to use MedChemica, an AI software application provider specialising in accelerating drug discoveries with AI, and Target Safety, an AI platform assessing drug safety profiles
- A portfolio of assessed deliverables – including a target validation case, a biomarker and patient subgroup analysis, and a capstone AI roadmap – that demonstrates the value of AI in the drug R&D pipeline to your organisation
- Ethical frameworks that propose AI as a tool – not one that replaces scientific rigour, but one that makes each stage of R&D more efficient and more defensible in the context of a highly regulated pipeline
OVERALL INTENDED LEARNING OUTCOME
On completion of the programme, you will be ready to deploy AI tools in your day-to-day work in drug R&D. You will be able to oversee the adoption of emerging AI tools in your R&D pipeline while upholding the strict ethical codes associated with using AI in pharma and biotech industries.
COURSE CONTENT
The online programme curriculum includes six modules spread over six weeks:
Module 1: Introduction to AI and drug discovery
- Explain how AI technology is applied across drug discovery and healthcare data management
- Describe the core principles of AI, machine learning and neural networks
- Examine how LLMs and AI agents are used in healthcare
- Investigate principles of data governance and management
Module 2: Target selection and validation
- Determine the validity of a biological target against a defined, unmet clinical need using AI-driven analytical models
- Analyse genomic data using AI
- Evaluate how AI-identified therapeutic targets are validated
- Consolidate the structure, criteria and core elements required for evidence-based cases for a validated target
Module 3: AI-powered therapeutics design
- Examine the basic principles of medicinal chemistry in drug design
- Explore how to use AI to design novel drug candidates for an identified target
- Evaluate AI-generated drug candidate designs and output across target structure, small molecules and biologics
Module 4: AI for personalised medicine
- Discuss how data supports biomarker discovery, antiviral development and modern epidemiology
- Analyse clinical and omics data using AI
Module 5: The in silico trial: Connecting therapeutics to patient
- Explore how predictive AI models simulate therapeutic effects
- Analyse the potential safety profiles on different patient populations using AI
Module 6: Future directions and responsible AI
- Explore the ethical consideration of AI in medicine
- Assess the regulatory governance consideration of AI in medicine
ELIGIBILITY
This programme is designed for technical and non-technical R&D professionals, scientists and clinician-researchers who want to learn how to adopt or oversee the implementation of computational AI tools in the drug R&D pipeline. This includes:
- Professionals working directly in R&D who want to learn how AI is being used to accelerate drug discoveries
- Industrial and academic biologists and chemists who want to keep pace with how AI is reshaping research in their fields
- MDs, PharmDs and clinical development professionals who want to safely and efficiently close the gap between laboratory research and bedside patient care
No prior coding experience is required.
FEES AND FUNDING
All information can be found on the Graduate Admissions website. Confirmed changes in fees and funding eligibility can also be found on the University fees and funding webpage.
Your Programme Director
Alex Bullock
Professor of Structural and Chemical Biology, Oxford
Alex Bullock is Professor of Structural and Chemical Biology at the Centre for Medicines Discovery at the University of Oxford. His work has defined a novel cancer mechanism of E3 ligase neofunction and identified a drug candidate for fibrodysplasia ossificans progressiva that is currently in Phase II clinical trials. Prior to this, he spent over 15 years at the Structural Genomics Consortium, an international public-private partnership producing structures, assays and inhibitors for novel drug targets – particularly protein kinases and Cullin-RING E3 ligases.
He undertook doctoral training at the University of Cambridge with Sir Alan Fersht before holding Wellcome fellowship positions with Sir Peter Ratcliffe on the VHL-HIF pathways (University of Oxford) and in the US with Professor David Barker on Rosetta-based protein design (University of Washington, Seattle).