GLP-1 Receptor Positive Allosteric Modulators

drTarget applies machine learning models on ChEMBL and PChem results to perform a virtual screening on 2.3M molecules finding >37k potential positive allosteric modulators with a predicted activation score.

drTarget uses 400k values from a full curve GLP1R inverse agonists screens stored in PChem DB plus all ChEMBL GLP1R positive allosteric modulation assays and literature references describing activities of well characterized GLP1R PAMs.

Scatter plot representing a GLP1R PAM vs agonist score selection dashboard. Dots selected in the upper graph are shown as full curves representing PAM, agonist and cytotox activity in the inverse agonists, agonists and cytotoxicity PChem screens.

A set of  random forest regression and classification ensembles is applied to produce a unique GLP1R PAM score and submitted to cross validation.

Comparison between actual and predicted activity scores for the resulting combined model.

ML models are applied to the whole ChEMBL DB content and aggregated to the molecule level to calculate a predicted GLP1R activation score. 

And finally, drTarget analyzes all activities of seelcted active PAMs on relevant ChEMBL phenotypes. 

With a network graph here…

…Or a treeMap

Click below to examine particular examples of these interactions with some analogues of known GLP1R PAMsor some selected molecular series. 

Find out effects of GLP1R predicted active compounds on gene expression and their occurrence in foods. 

Or explore GLP1R associations with diseases, scored by OpenTargets algorithms.