BiasNet: A model to predict ligand bias towards GPCR signaling

18 March 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it is therapeutically advantageous for a drug to be G Protein biased or β-Arrestin (β-Arr) 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 leads to bias. More specifically, we used BiasDB, a database containing information about GPCR biased ligands and all ligands which show a (β-Arr) / G protein bias or a G protein / β-Arr bias are considered for the study. Four machine learning models were trained on these ligands to classify them. The features which were most important for training the models were analyzed. Two of these features (number of secondary amines and number of aromatic amines) were more prevalent in β-Arr biased ligands. After training a Random Forest model on HierS scaffolds, we found five scaffolds which demonstrated G protein or β-Arr 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 a user-provided SMILES patterns. Our web implementation is available at:


Biased Signaling
Machine Learning

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.