G-protein-coupled receptors (GPCRs) are the most important class of drug targets. GPCRs are the biggest family of cell surface receptors and represent approximately 4-5% of the protein-coding human genome. Several physiological functions are carried by them, including neurotransmitters and hormone receptors. They are vital indicators of disease and can be used to diagnose and predict. Furthermore, GPCR targeting drugs comprise more than half of all current drugs, including 25 percent of the top 100 best-selling drugs.

 GPCRs are one of the most challenging protein targets in structural biology due to their extremely dynamic nature and their ability to exhibit different conformational states during their interactions with ligands and modulating proteins. This protein superfamily consists of more than 800 members, and only a small number of unique structures have been reported. The obtained structures represent a single conformational state, while GPCRs are highly dynamic. Some GPCRs are orphans, meaning their natural substrate ligands or agonists are unknown.

GPCRs are abnormally expressed by cancer cells. Tumorigenesis requires an urgent demonstration of which GPCRs are altered by which ligands and identifying their associated downstream signalling mechanisms. GPCRs represent the most important drug targets, but their applications as cancer targets remain underexploited to the extent that few anticancer agents that interfere with GPCR-mediated signalling are used in clinical practice. There is some evidence suggesting that GPCRs can serve as effective cancer treatment targets.

Knowing the conformational changes that occur during GPCRs’ functional dynamics and understanding how they function will probably facilitate the development of new targets and novel treatments for cancer patients. On the other hand, ML can be applied as an AI technique for drug discovery and cancer research by analysing big data obtained by computational techniques. However, this type of sophisticated function requires the use of computational techniques extensively.