Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning
Schaller, KayShow all
- Journal Article
The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR‐targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter‐propagation artificial neural network, a k‐nearest neighbor learner, and a three‐dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top‐ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit‐to‐lead expansion. Show more
Journal / seriesChemistryOpen
Pages / Article No.
SubjectDrug design; Drug discovery; Neural networks; Nuclear receptors; Virtual screening
Organisational unit03852 - Schneider, Gisbert / Schneider, Gisbert
177477 - Beyond fragment-based drug design: from natural products to small synthetic mimetics through novel holistic molecular representations (SNF)
NotesThis article was also published in the journal "Medical Chemistry".
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