The 2024-2025 Recruitment Round is now open!
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
Below is a list of projects that were available for the 2024-25 recruitment round.
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:
Please note, applications submitted under Round One are currently being considered and projects outlined below are subject to change as a result of the outcome.
H1: AI-driven Omics Data Integration and Transfer Learning for Target and Biomarker Identification
The rapid expansion of available multi-Omics data and AI technology (convolutional neural networks, large language models) provides the basis for developing computational approaches to identify novel drug targets and associated target engagement biomarkers in (pre)clinical models.
Heterogeneous data sources and non-overlapping samples are challenging aspects of available data integration methods. Deep learning models provide an opportunity to overcome current limitations in using data across data modalities (genetic, transcriptomic, proteomic, lipidomic) and resources (public databases, in-house data).
AI models incorporating specific biological correlations from large (public) data sets, for example disease-related dysregulation or modulation of molecular interaction patterns, allow for transfer learning, whereby the learned biological information is combined with information from (local) data sets of interest.
This PhD will explore the potential of AI methodologies and deep learning solutions to mine large multi-omics datasets providing novel mechanistic insights and actionable candidate biomarkers with application to drug discovery.
Using deep learning models on publicly available and in house generated datasets in neurodegenerative disease and inflammatory bowel disease (IBD) you will develop novel AI models enabling a streamlined interpretation of multi-omics data. This will support our understanding of biological disease mechanisms and enable the identification of new targets and biomarkers.
The successful candidate is enthusiastic about exploring Deep Learning models in the context of data-driven drug development in a fast-paced environment and highly collaborative team. The ideal candidate will have prior knowledge in programming, machine learning and/or biology and bioinformatics.
Sosei Heptares is a world-leader in stabilizing G Protein-Coupled Receptors (GPCRs). Our patent-protected technologies enable unique structural insights into GPCRs as drug targets. As such, we have the ability and the know-how to design new therapeutic agents with optimized pharmacology using Structure-Based Drug Design (SBDD). Our approach delivers drug candidates with improved physicochemical properties, as well as the potential for enhanced safety and efficacy profiles, and could reduce clinical attrition rates.
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 2024). 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 £20,622) and a consumable allowance for a period of 4-years (pro-rata for part-time).
Visit the Apply Pages for details on how to submit an application.
Supervisors: Harmen Draisma - Senior Scientist, Bioinformatics, Sosei Heptares Conrad Bessant - Professor of Bioinformatics, School for Biological and Behavioural Sciences, QMU
Project Partner:Sosei Heptares
E1: Combining CRISPR-screens and AI to Identify Novel Therapeutic Strategies to Target Cancer-Stroma Interactions
As tumours progress, the relationship between cancer cells and the tumour microenvironment (TME) evolves to promote growth and evade anti-tumour immunity. This multidisciplinary project will combine the fields of cancer biology, bioinformatics and artificial intelligence to identify tractable targets for manipulating the cancer-TME interaction for clinical translation. This requires the development of new methodologies capable of modelling fundamental biological mechanisms."
During tumorigenesis, malignant phenotypes evolve along with molecular susceptibilities that can be targeted by therapies. Initiatives like the cancer dependency map (DepMap) aim to systematically unravel these vulnerabilities through extensive loss-of-function (CRISPR) screens across cancer cell lines. However, these studies are not performed in a microenvironment that is representative of a patient cancer and focus solely on cancer cells themselves. This project will address this limitation by exploring strategies to target the interactions between cancer, stromal and immune cells.
Working with colleagues from the BCI and Exscientia, a leading AI drug discovery company, you will integrate in-house and publicly available genome-wide CRISPR screen, proteomic and transcriptomic datasets. To delineate novel targetable pro-tumourigenic pathways, you will apply cutting-edge machine learning methods to construct interpretable models of cancer-stroma-immune interactions. Your computational predictions will be tested by colleagues in the Cameron laboratory in state-of-the-art 3D heterotypic co-cultures that accurately mimic TMEs. Results from lab experiments will feed a prediction-validation learning loop, ensuring the continual improvement of computational models and resulting target predictions. Promising new targets with the potential to impede tumour progression and manipulate the immune suppressive microenvironment will be exploited in concert with Exscientia’s AI-driven target identification, prioritization and drug design capabilities.
We are seeking a highly motivated candidate eager to apply computational methods to complex biological questions. The ideal candidate will bridge cancer biology and data science, with a solid understanding of molecular biology and some experience performing data wrangling, statistics and/or machine learning in a modern programming language (e.g., R/Python). The successful candidate will benefit from mentorship and training provided by the BCI and Exscientia, and gain exposure to modern AI-enhanced oncology research and target identification efforts.
Supervisors:Dr Otto Morris - Senior Biological Data Scientist, Exscientia
Dr Angus Cameron - Reader in Cell Signalling and Tumour Cell Biology, arts Cancer Institute, QMUL
Professor Pedro Cutillas - Professor of Cell Signalling and Proteomics, QMUL, Barts Cancer Institute, QMUL
Project Partner:Exscientia
F1: AI-based Identification of New Drug Targets for Personalised Oncology
The genes of cancer cells acquire somatic alterations at a rate higher than non-cancer cells. Some of these altered genes are essential to drive the different phases of cancer evolution and are therefore called cancer drivers. Identifying cancer drivers is one of the major goals of cancer biology because their knowledge highly improves our understanding of cancer mechanisms and expands the repertoire of possible therapeutic interventions. Cancer drivers are however challenging to find because they are only a small minority of all cancer altered genes. Recently, our group has developed a machine learning method for cancer driver identification in individual patients. Our approach is first trained to learn the features of a well characterised set of cancer drivers. It then predicts as cancer drivers the altered genes in individual patients that best resemble these features. We have applied our approach to almost 10,000 cancer samples from 34 cancer types, building a vast repertoire of cancer drivers. In this project we aim to refine this knowledge towards the prediction of new drug targets for personalised oncology. The student will first extract useful information on druggability as well as known drug targets from curated resources including DrugBank, GDSC, Meta-Knowledgebase, DGIdb, STICH, CTD. They will then build a new classifier aimed to predict new potential targets among all available cancer drivers. Predictions will include either the repurposing of already available drugs as anticancer agents in specific contexts, or the identification of new targets for the development of new drugs.
The ideal candidate will have a background in computer science or statistics. Some knowledge in artificial intelligence classification and/or cancer biology will be favourable.
This studentship award will cover a UKRI Stipend (currently £20,622), a consumable allowance, and UK tuition fees only, for a period of 4 years (pro-rate for part-time study). To be classed as home for fee purposes, candidates typically need to have unrestricted access on how long they can remain in the UK (i.e. are a British National, have settled, or pre-settled status, have indefinite leave to remain etc.) and have been living in the UK for the 3 years immediately prior to studentship starting. Fee status is determined by the Admissions department at the point of application. Candidates who would be classed as overseas are welcome to apply for this opportunity, however must state in their application other funding they have secured to cover the difference in tuition fees.
Visit the Apply Pages for further details on how to submit an application.
Supervisors:Francesca Ciccarelli - Lead of the Centre for Cancer Genomics and Computational Biology, Bart's Cancer Institute