Skip to main content

Leveraging Artificial Intelligence for Deorphanisation of G protein-coupled Receptors: Predictive Models and Ligand Design (scholarship provided)

Primary supervisor

Teresa Wang


  • Anh TN Nguyen

The objective of this project is to use machine learning techniques to help with the drug discovery by modelling structural and sequential data. This project is supported by a supervision team with both machine learning background and Pharmaceutical background with real Pharmaceutical data labeled and ready to use. As we all know, Monash University ranks #2 in the world for Pharmacy and Pharmacology and drug discovery is of significant social benefit. This is a perfect opportunity to use your knowledge in machine learning to make real-world impact with the support of a world renowned institute. Moreover, we have real data labeled and ready to use. So, do not hesitant and come to join us if you are passionate about using your technical knowledge to make a real-world difference. 

G protein-coupled receptors (GPCRs) represent the largest family of cell surface receptors that respond to a variety of stimuli, from light to hormones, to facilitate cellular communication. Their inherent signal promiscuity and diversity link them to a myriad of diseases, accounting for over 30% of all currently marketed therapeutic agents. Despite their significance, a substantial number of these GPCRs are designated as orphan receptors, with their endogenous ligands and physiological functions remaining elusive. This project aims to bridge this knowledge gap by leveraging artificial intelligence (AI) to predict constitutive activity, identify endogenous ligands, and design novel ligands for orphan GPCRs, leading to their deorphanisation and expanding the potential drug targets.


Application Due date: 25th/Sep/2023 unless it is filled before the due date


Required knowledge

Practical knowledge of using modern deep learning methods as well as extensive experience with Python programming.

Standard Machine Learning and Artificial Intelligence as covered in masters or advanced undergraduate subjects.

Good understanding of Machine Learning principles.

Good English communication skills.

It is desirable if the candidate has experience in modelling structural/graph and temporal/sequence data. 

Project funding

Project based scholarship

Learn more about minimum entry requirements.