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.
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