Bayesian Model for the Classification of GPCR Agonists and Antagonists.gif

G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are known to be
targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are
mainly classified as agonists and antagonists. The purpose of this study is to develop a Bayesian classification model,
that can predict a compound as either human GPCR agonist or antagonist. Total 6627 compounds experimentally deter-
mined as either GPCR agonists or antagonists covering all the classes of GPCRs were gathered to comprise the data-
set. This model distinguishes GPCR agonists from GPCR antagonists by using chemical fingerprint, FCFP_6. The
model revealed distinctive structural characteristics between agonistic and antagonistic compounds: in general, 1)
GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic
amines. This model showed very good discriminative ability in general, with pretty good discriminant statistics for the
training set (accuracy: 90.1%) and a good predictive ability for the test set (accuracy: 89.2%). Also, receiver operat-
ing characteristic (ROC) plot showed the area under the curve (AUC) to be 0.957, and Matthew’s Correlation Coeffi-
cient (MCC) value was 0.803. The quality of our model suggests that it could aid to classify the compounds as either
GPCR agonists or antagonists, especially in the early stages of the drug discovery process.