Front Cover: Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning (ChemistryOpen 1/2019). Issue 1 (30th November 2018)

• Record Type: Journal Article
• Authors: Merk, Daniel; Grisoni, Francesca; Schaller, Kay; Friedrich, Lukas; Schneider, Gisbert
• Is Part Of: ChemistryOpen. Volume 8:Issue 1(2019)
• Page Start: 1
• Page End: 1
• Publication Date: 2018-11-30
• Subjects: drug design -- drug discovery -- neural networks -- nuclear receptors -- virtual screening; Chemistry -- Periodicals; 540; 540.5
• DOI: 10.1002/open.201800271
• Languages: English
• ISSNs: 2191-1363

Abstract

Abstract : The Front Cover shows the application of machine‐learning methods to expand the chemical space of farnesoid X receptor (FXR)‐targeting small molecules, by employing an ensemble of three complementary machine‐learning approaches (counter‐propagation artificial neural network, k‐nearest neighbor learner, and three‐dimensional pharmacophore model). The ensemble machine‐learning model identified six new FXR modulators from a library of 3 million compounds. These computationally identified bioactive compounds possess four novel scaffolds and appreciably expand the chemical space of known FXR modulators. More information can be found in the Full Paper by D. Merk et al. onpage 7 in Issue 1, 2019 (DOI:10.1002/open.201800156 ).