Environment
Imperial College London is consistently rated as one of the World’s leading universities (https://www.topuniversities.com/university-rankings/world-university-rankings/2022). Imperial’s Department of Bioengineering has a strong international profile and currently hosts about 230 PhD students, thus providing a supportive and stimulating environment to carry out a PhD. It was declared UK’s top department of bioengineering for research in the latest Research Excellence Framework. The department has a strong computational and theoretical modelling theme, and participates in Imperial networks such as the AI Network, the Centre for Drug Discovery Science and the CRUK Convergence Science Centre (https://www.convergencesciencecentre.ac.uk/) between Imperial (https://www.imperial.ac.uk/) and ICR (https://www.icr.ac.uk/).
Project
Predictive models built with artificial intelligence (AI) methods are powerful tools to discover molecules with the potential to become drugs to treat a given disease. These models can leverage training datasets to identify such drug leads by computational (virtual) screening of massive libraries of molecules. In particular, AI models can be trained on pharmaco-omic datasets to predict the phenotypic activity of molecules on cancer cell lines. Despite important successes, there are major challenges limiting the potential of such AI models. Some are specific to this problem (e.g. how to augment pharmaco-omic datasets in a way that improves the performance of these models). Other challenges are also found in other supervised learning problems (e.g. accurately delimiting the applicability domain of the model). This PhD project aims at making progress towards overcoming these challenges using both synthetic and real datasets.
Selection criteria - Essential
University degree/s awarded in an area directly relevant to the project.
Courses in the application of machine learning algorithms to scientific problems.
Excellent grades in first and/or master degrees, especially in their research projects, with a major focus on computational analysis of data.
Skilled in the implementation of Python or R scripts for scientific data analysis.
English language (https://www.imperial.ac.uk/study/pg/apply/requirements/english/).
Selection criteria - Desirable
Research projects in the application of supervised learning to solve real-world problems in the context of biomedical research, especially virtual screening.
Exposure to open-source chemical informatics toolkits (e.g. RDKit, OpenBabel), machine learning platforms (e.g. DeepChem, TorchDrug, Scikit-Learn, Caret), and/or medicinal chemistry databases (e.g. ChEMBL, PubChem, ZINC).
Exposure to the application of machine learning algorithms to drug design, e.g. QSAR.
Familiarity with cancer pharmaco-omics databases (e.g. GDSC, CCLE, CTRP).
How to apply
Candidates must send an email with their CV, grades for each held university degree and a covering letter (maximum two pages) to [Email Address Removed] with subject line “PhD in AI for PVS”. This letter must explain how they meet the essential selection criteria, which desirable selection criteria are also met and how this position would fit in their future career plans. This email must also state the names and emails of two scientists involved in assessing their academic performance, who are willing to provide a reference. Please also mention in the letter where did you see this position
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