AUTHOR=Yamane Haruki , Ishida Takashi 

TITLE=Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs

JOURNAL=Frontiers in Bioinformatics

VOLUME=Volume 3 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2023.1193025

DOI=10.3389/fbinf.2023.1193025

ISSN=2673-7647

ABSTRACT=Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs and are important for drug discovery. 
Various computational approaches have been applied to predict their ligands. Among them, the compound-protein interaction (CPI) prediction has been considered one of the most suitable for class A GPCRs due to a large number of orphan receptors.
However, the accuracy of CPI prediction is still insufficient. 
The current CPI prediction model employs the whole protein sequence as the input. 
However, only a few transmembrane helices of class A GPCRs play a critical role in ligand binding.
Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family.