Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

Daniel Merk; Francesca Grisoni; Kay Schaller; Lukas Friedrich; Gisbert Schneider

doi:10.1002/open.201800270

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

Daniel Merk, Francesca Grisoni, Kay Schaller, Lukas Friedrich, Gisbert Schneider

Research output: Contribution to journal › Comment/Letter to the editor › Academic › peer-review

1 Citation (Scopus)

Abstract

Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates 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). Read the full text of their Full Paper at 10.1002/open.201800156.

Original language	English
Pages (from-to)	3
Number of pages	1
Journal	ChemistryOpen
Volume	8
Issue number	1
DOIs	https://doi.org/10.1002/open.201800270
Publication status	Published - Jan 2019
Externally published	Yes

Bibliographical note

Funding Information:
This research was financially supported by the Swiss National Foundation (grant no. IZSEZ0 177477). D.M. was supported by an ETH Zurich Postdoctoral Fellowship (grant no. 16-2 FEL-07).

Publisher Copyright:
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Funding

This research was financially supported by the Swiss National Foundation (grant no. IZSEZ0 177477). D.M. was supported by an ETH Zurich Postdoctoral Fellowship (grant no. 16-2 FEL-07).

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10.1002/open.201800270

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title = "Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning",

abstract = "Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates 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). Read the full text of their Full Paper at 10.1002/open.201800156.",

author = "Daniel Merk and Francesca Grisoni and Kay Schaller and Lukas Friedrich and Gisbert Schneider",

note = "Funding Information: This research was financially supported by the Swiss National Foundation (grant no. IZSEZ0 177477). D.M. was supported by an ETH Zurich Postdoctoral Fellowship (grant no. 16-2 FEL-07). Publisher Copyright: {\textcopyright} 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim",

year = "2019",

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doi = "10.1002/open.201800270",

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AU - Merk, Daniel

AU - Grisoni, Francesca

AU - Schaller, Kay

AU - Friedrich, Lukas

AU - Schneider, Gisbert

N1 - Funding Information: This research was financially supported by the Swiss National Foundation (grant no. IZSEZ0 177477). D.M. was supported by an ETH Zurich Postdoctoral Fellowship (grant no. 16-2 FEL-07). Publisher Copyright: © 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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N2 - Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates 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). Read the full text of their Full Paper at 10.1002/open.201800156.

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Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

Abstract

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