Prioritising Drug Targets in non-canonical pathways from multi-omic data

There exists a large and rapidly growing body of publicly available datasets from studies that monitor cellular response to perturbation using methods such as bulk and single cell transcriptomics, LS-MS/MS proteomics and phosphoproteomics, metabolomics and epigenetics. While individual studies are useful in their own right, large scale meta-analysis of data across studies promises more significant breakthroughs, such as the discovery of valuable new drug target leads.

The aim of this project is to develop and apply AI methodologies to identify potential drug targets from large collections of aggregated omics data. Specifically, you will explore the use of supervised machine learning approaches to learn patterns of behaviour exhibited by proven drug targets and use the results of this work to build predictors capable of ranking other molecules according to their drug target potential. Ultimately, the aim will be to apply your newly developed AI approach to in-house data held by MSD.

The project offers extensive freedom in terms of the AI methods used and the data sets studied, so provides an excellent opportunity to gain hands-on experience of the latest machine learning algorithms, and to evaluate the value of different types of omics data.