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