Applications for the 2025-26 intake of the AI for Drug Discovery are now open and the available projects are listed below.
We are delighted in this cross-faculty and cross-disciplinary training programme with our industrial partners to train the next generation of drug discovery researchers— Mike Barnes, Professor of Bioinformatics. The William Harvey Research Institute, Faculty of Medicine and Dentistry
Each project has a supervisor based at Queen Mary, and engagement from Industry, including the option for a placement. The level of industry engagement varies depending on the nature of the project. We suggest you review each project description to learn more about the proposed research. Once you have identified your top project, you can submit an application via the Apply page. Note, you will be asked to identify your chosen project, and a maximum of 1 other project; you cannot apply for more than 2 projects, so we recommend you consider your choice carefully, ensuring what it is the right fit for you and your research aspirations.
Points to consider when reviewing projects:
New Projects may be added during the recruitment period, and Projects will be removed if allocated during the Round 1 stage.
Development of AI framework for iPSC image analysis and integration with transcriptomics
The Draviam lab aims to understand the molecular principles that govern cell division and the consequence of its failure when cells transition between states during differentiation. Together with MSD, the project aims to focus on developing AI methods to track iPSC (induced Pluripotent Stem Cells) differentiation to neural precursors, astrocytes and neurons. During the process of differentiation, quiescence and senescence are two cell dormancy states with distinct cell fates and transcriptomic statuses. However, these two states of dormancy have similar nuclear shape and size presentation (in images) which we aim to separate by developing a DL-based image analysis framework that tracks their behaviour through time. The ideal candidate is expected to have a strong background in image analysis, development of ML/DL methods, a keen interest in biosciences and provable experience in teamwork.
Eligibility and Applying
This project is part of the UKRI/BBSRC AI for Drug Discovery Progamme, and successful candidates will join a cohort of students working on complementary projects in the AI for Drug Discovery space.
We are looking for highly motivated individuals who are passionate about contributing to new discoveries in drug discovery bioscience through the application of the latest techniques in AI and data science. Ideal candidates will have a grounding in both a natural science and data science, e.g. through a Master's degree or work experience in a subject such as bioinformatics or computational chemistry. Alternatively, you may have, for example, a first-class degree in computer science followed by biochemistry experience, or vice versa (qualifications and evidence thereof must be obtained before October 2025). You will be confident in performing data wrangling and analysis in a language such as Python, R or C++. Effective communication skills are essential.
We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.
This studentship award can cover full tuition fees, UKRI stipend (currently £21,637) and a consumable allowance for a period of 4-years (pro-rata for part-time).
Supervisors: Viji Draviam - Professor of Quantitative Cell and Molecular Biology and Director of Industrial Innovation, School of Biological and Behavioural Sciences, QMUL Wei Wei - Associate Principal Scientist, MSD
Project Partner: MSD
Unveiling the Dark Genome: AI-Driven Discovery of Novel Proteins and Variants in Microtubule-Associated Genes
The human genome harbors vast untapped potential within its "dark" regions, comprising 98% of our genetic material. This project, a collaboration between the Draviam laboratory and NonExomics INC, aims to revolutionise our understanding of the dark genome, focusing on microtubule-associated protein (MAP) encoding gene loci.
Recent advancements in proteogenomics have revealed the existence of numerous noncanonical proteins encoded by previously overlooked genomic regions. These proteins, derived from long non-coding RNAs, circular RNAs, and alternative open reading frames, play crucial roles in cellular processes and disease mechanisms. By leveraging cutting-edge AI methods, we will:
The project will utilize UK Biobank data to build genotype-phenotype links based on early cancer onset, subfertility, or developmental defects. By combining bioinformatic searches with AI methods we aim to elucidate the functional significance of naturally occurring variants in chromosome segregation genes.
This research has the potential to:
We seek applicants with a strong background in AI methods, genomics, proteomics, and protein structure analysis. This interdisciplinary approach promises to unlock new frontiers in personalized medicine and drug discovery by harnessing the hidden potential of the dark genome.
Visit the Apply Pages for details on how to submit an application.
Supervisors: Professor Viji Draviam - Professor of Quantitative Cell and Molecular Biology, School of Biological and Behavioural Sciences, QMUL
Project Partner: NonExomics INC
Integrating Multi-Modal AI Approaches for Novel CNS Drug Target Discovery in Pre-Alzheimer's and Neuropsychiatric Disorders
This research project aims to advance CNS drug target discovery by developing advanced multi-modal AI methodologies to validate BullFrog AI’s comprehensive analysis of post-mortem brain tissue data from the Liber Institute for Brain Development (LIBD) in additional QMUL patient cohorts.
Through an exclusive commercial agreement, BullFrog AI has access to one of the most extensive and diverse brain tissue datasets available, enabling novel approaches to identify therapeutic targets at the intersection of neuropsychiatric disorders and Alzheimer's disease risk. The LIBD dataset comprises 2,818 tissue samples from 1,021 unique cases spanning schizophrenia, major depressive disorder, and bipolar disorder, collected from five brain regions including the hippocampus, dentate gyrus, caudate, and prefrontal cortex. A significant subset includes 297 cases with elevated Alzheimer's disease risk based on APOE variants. The dataset's diversity across ethnic/racial backgrounds, gender representation, and lifespan stages provides a robust foundation for understanding disease mechanisms.
BullFrog AI's analysis of the LIBD data using their generative and causal AI platforms has already identified amyloid precursor genes and other neurodegeneration-associated genes as putative causal drivers, particularly within patient clusters showing higher rates of depression. These findings suggest molecular links between neuropsychiatric conditions and Alzheimer's disease risk.
This PhD projects proposes to validate these finding, utilising multi-omics and digital pathology approaches to analyze QMUL's independent patient datasets. This multi-modal AI validation strategy will leverage DERI's digital pathology capabilities to confirm identified molecular subtypes and potential drug targets in high-risk populations. The project outcome aims to substantiate BullFrog AI's discoveries through computational approaches using QMUL's independent data resources.
Visit the Apply Pages for details on how to submit an application
Dr Qianni Zhang - Senior Lecturer, School of Electronic Engineering and Computer Science, QMUL
Dr Tom Chittenden - Chief Scientific Officer, BullFrog AI
Unlocking Drug Discovery Using AI to Explore use of Global Plant Diversity
Plants have provided, or inspired, the development of important pharmaceutical drugs. Yet, of the estimated ~350,000 vascular plant species known globally, the chemical components of the vast majority remain largely unexplored scientifically, and the existing chemical information is scattered across the literature. This hampers the development of plant-based solutions to some of the most pressing challenges facing humanity, including those highlighted by Sustainable Development Goal 3 (Good health and well-being) established by the United Nations.
This PhD project aims to leverage information accumulated regarding the wider use of plants for health benefits and recent development in the field of Artificial Intelligence, including machine learning and deep learning (e.g., neural networks as in computer vision and large language models).
The study will: 1) address the challenges inherent in locating and extracting information on the use and chemical properties of plants, and 2) assess potential evolutionary, ecological and cultural determinants of the chemical composition of plants to predict candidate species for future chemical screening and drug discovery. The project will initially focus on chemical compounds and plant taxonomic groups previously investigated by the team of supervisors, including e.g., anticancer flavonoids from the Lamiaceae family, or antimalarial alkaloids from the Apocynaceae family.
The successful candidate will benefit from training by an interdisciplinary team of scientists across QMUL’s Biology department and the Digital Environment Research Institute (DERI), and from a strong partnership with Royal Botanic Gardens, Kew, especially the Plants for Health programme (https://www.kew.org/science/our-science/projects/plants-for-health).
The ideal candidate will have a background either in AI, computer science, biostatistics, phytochemistry, ecology, evolution, and/or plant sciences, with a strong interest for interdisciplinarity and experience in both natural science and data science. We expect the student to be confident in handling and analyzing large datasets and to have programming experience in Python and/or R.
Visit the Apply Pages for further details on how to submit an application.
Supervisors:
Dr Samuel Pironon - Lecturer in Conservation Biology, School of Biological and Behavioural Sciences, QMUL
Professor Cedric John - Professor and Head of Data Science for the Environment and Sustainability, Digital Environment Research Institute, QMUL
Industry Partner: Royal Botanic Gardens, Kew
Uncovering Mechanisms of mTOR Inhibition Resistance through Drug Repurposing, Interactomics and AI
The mechanistic target of rapamycin (mTOR) is an evolutionarily conserved kinase that acts as a rheostat of energy, regulating cellular processes and decisions including metabolism and ageing. Deregulation and aberrant activation of mTOR promotes age-related diseases and tumour metastasis and is a key driver of drug resistance. Some of the mTOR inhibitors have been developed. Due to the fundamental roles of mTOR within all eukaryotes, resistance to mTOR inhibition itself is a major problem. Nevertheless, the involved mechanisms that result in uncoupling cellular growth from mTOR activity are still poorly defined. Such knowledge is critical to decidedly address the issue of resistance to mTOR inhibition in ageing and disease.
Aims: We have identified conserved genes that when mutated render cells resistant to mTOR inhibitors.
Using systems biology approaches, robotics, available multi-omics resources and synthetic biology the main aims of the project are to:
Outcomes:
Benefits to student from industrial collaborations:
Supervisors
Dr Charalampos (Babis) Rallis - Reader in Genetics, Genomics and Fundamental Cell Biology, Centre for Molecular Cell Biology, School of Biological and Behavioural Sciences.
Dr Radu Zabet - Senior Lecturer in Computational Biology, Centre of Genomics and Child Health, Blizard Institute
Dr Phil Kirk - Senior Scientist, Singer Instruments
Industry partners:
Clinical Services International (CSI)
Singer Instruments