Data-driven engineering of novel drugs by machine-learning-derived understanding of the molecular basis for drug action

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This project will pave the way for Artificial Intelligence-based data-driven drug "engineering" that will shape a new generation of more efficacious drugs with fewer side-effects. G protein coupled receptors (GPCRs) are the target for around one-third of drugs, but these typically activate a number of separate desirable and undesirable signalling pathways, reducing the efficacy of the therapy and increasing undesirable side effects. To design better drugs, we must understand the mechanisms of drug action in much greater detail. In this project, we will develop state-of-the art AI and machine learning approaches to combine unique pathway activation data from high-throughput alanine scanning with structural information and data from high-throughput screens of molecular compound libraries to 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.