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Digital Environment Research Institute (DERI)

Projects available

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:

  • Is the project a good fit for my research experience to-date, and my research interests?
  • Do I have the necessary background knowledge, or could I reasonably acquire this through targeted training on the programme?
  • What attracts me to this project, and which part of the project most excites me?
  • Does the supervisory team seem a good fit for me, and what makes me want to work with them?

Development of AI framework for iPSC image analysis and integration

M1: 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 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 further details on how to submit an application. 

Supervisors:  
Viji Draviam - Professor of Quantitative Cell and Molecular Biology and Director of Industrial Innovation, School of Biological and Behavioural Sciences, QMUL  
Victor Neduva - Senior Director, Genome and Biomarker Sciences, MSD  

Project Partner:  
MSD 

 

AI-driven Omics Data Integration for Target and Biomarker Identification

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

 

Reducing Drug Cardiac Toxicity Using Molecular Simulations and Machine Learning

S1: Reducing Drug Cardiac Toxicity Using Molecular Simulations and Machine Learning 

The potassium channel hERG (Kv11.1) is one of the main drug off-targets: many potentially beneficial, drug-like molecules interact with hERG in an unwanted manner, leading to severe cardiotoxic side effects, and limiting the development of safe drugs. Although many computational models for evaluating hERG toxicity exist, often they lack accuracy when generalizing across an effectively limitless chemical space. As recently shown in several drug discovery projects, rigorous physics-based approaches offer not only high accuracy binding affinity prediction, but also the explanation of the physical nature of the observed effects, enabling further rational drug design. 

In this project, we will combine molecular simulations and free energy calculations to first validate and benchmark these methods against databases of known hERG inhibitors. Next, they will be applied to study ligand chemistries that have not yet been tested against hERG. The generated data will provide a basis to train machine learning models to efficiently replace costly physics based calculations in predicting cardiac toxicity of novel molecules. Resulting models will be tested and used by the industrial collaborator in their ongoing real-life drug development campaigns. 

We are looking for candidates with keen interests in developing and applying computational tools to investigate complex questions in natural sciences. Experience in computational chemistry, biomolecular simulations, and/or computer-aided drug discovery is desirable. The position requires excellent interpersonal, oral and written communication skills in a collaborative, interdisciplinary drug discovery environment. The successful candidate will have an opportunity to learn cutting-edge computational techniques used in pharmaceutical research and industry, as well as develop a state-of-the-art method helping reduce the cardiac cytotoxicity of novel drugs. 

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 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 details on how to submit an application

Supervisors:

Wojciech Kopec - Lecturer in Computational Pharmaceutical Chemistry, School of Physical and Chemical Sciences, QMUL 

Dr. Vytautas Gapsys - Senior Scientist, Janssen Belgium 

Project Partner: Janssen Belgium 

CRISPR-screens and AI to identify novel therapeutic strategies for cancer-stroma

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. 

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:
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 

 

AI-based identification of new drug targets for personalised oncology

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.

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 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

 

 

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