BiasNet: A Model to Predict Ligand Bias Toward GPCR Signaling

Signaling bias is a feature of many
G protein-coupled receptor
(GPCR) targeting drugs with potential clinical implications. Whether
it is therapeutically advantageous for a drug to be G protein biased
or β-arrestin biased depends on the context of the signaling
pathway. Here, we explored GPCR ligands that exhibit biased signaling
to gain insights into scaffolds and pharmacophores that lead to bias.
More specifically, we considered BiasDB, a database containing information
about GPCR biased ligands, and focused our analysis on ligands which
show either a G protein or β-arrestin bias. Five different machine
learning models were trained on these ligands using 15 different sets
of features. Molecular fragments which were important for training
the models were analyzed. Two of these fragments (number of secondary
amines and number of aromatic amines) were more prevalent in β-arrestin
biased ligands. After training a random forest model on HierS scaffolds,
we found five scaffolds, which demonstrated G protein or β-arrestin
bias. We also conducted t-SNE clustering, observing correspondence
between unsupervised and supervised machine learning methods. To increase
the applicability of our work, we developed a web implementation of
our models, which can predict bias based on user-provided SMILES,
drug names, or PubChem CID. Our web implementation is available at:
drugdiscovery.utep.edu/biasnet.