Cheminformatics and Machine Learning approaches for GPCR Computer-Aided Drug Design

This project will focus on the development and application of cheminformatics, machine learning (ML), and Artificial Intelligence (AI) approaches for Computer-Aided Drug Design (CADD) for G Protein Coupled Receptors (GPCRs), the largest family of cell signalling transmembrane proteins that can be modulated by a plethora of chemical compounds. This project will use the information available in many heterogeneous types of protein-ligand interaction data to develop models that enable the design of efficacious therapeutic compounds targeting GPCRs. The project will make extensive use of public bioactivity data drawn both from the literature and patents, as well as experimentally determined structures of GPCR-ligand complexes.

You will use a variety of computational chemistry and cheminformatics techniques such as similarity assessment using 2D and 3D approaches, de novo design, quantitative structure-activity relationships for property prediction, molecular interaction fields, and protein-ligand docking. You will work on the development, evaluation, and optimization of novel approaches augmenting with experimental GPCR structural biology, chemical, and pharmacological data and state-of-the-art AI and ML techniques, including deep generative models, convolutional neural network models, and reinforcement learning. The ultimate goal will be techniques and approaches that can be applied to GPCR drug discovery projects as part of the Design-Make-Test-Analyse cycle.