Artificial Intelligence-based understanding of the molecular basis for drug action for data-driven engineering of novel therapies

This project will pave the way for Artificial Intelligence-based data-driven “engineering” of more efficacious drugs with fewer side-effects. Around one-third of drugs target G protein coupled receptors (GPCRs), but they typically activate multiple signalling pathways, reducing the efficacy and increasing undesirable side effects.

Designing better drugs requires a more detailed understanding of their mechanisms of action. We will develop state-of-the art AI and machine learning approaches to combine unique pathway activation data from alanine scanning with structural information and high-throughput screens of molecular compound libraries to understand the molecular basis of drug action in GPCRs, predict the action of a large range of drugs across GPCR classes, and predict ligands that will give rise to a desired signalling profile. Applicants should have a first degree in computer science (or similar), interests in machine learning, and enthusiasm for interdisciplinary work with the supervisors, Prof Veprintsev and Dr Styles.