hERG Me Out
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Paul Czodrowski
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000330097200005&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1021/ci400308z
- eISSN
- 1549-960X
- Externe Identifier
- Clarivate Analytics Document Solution ID: 295DQ
- PubMed Identifier: 23944269
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- JOURNAL OF CHEMICAL INFORMATION AND MODELING
- Paginierung
- 2240 - 2251
- Datum der Veröffentlichung
- 2013
- Status
- Published
- Titel
- hERG Me Out
- Sub types
- Article
- Ausgabe der Zeitschrift
- 53
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Autoren
- Paul Czodrowski
- DOI
- 10.1021/ci400308z
- eISSN
- 1549-960X
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- Journal of Chemical Information and Modeling
- Sprache
- en
- Online publication date
- 2013
- Paginierung
- 2240 - 2251
- Datum der Veröffentlichung
- 2013
- Status
- Published
- Herausgeber
- American Chemical Society (ACS)
- Herausgeber URL
- http://dx.doi.org/10.1021/ci400308z
- Datum der Datenerfassung
- 2023
- Titel
- hERG Me Out
- Ausgabe der Zeitschrift
- 53
Datenquelle: Crossref
- Abstract
- A detailed analysis of the hERG content inside the ChEMBL database is performed. The correlation between the outcome from binding assays and functional assays is probed. On the basis of descriptor distributions, design paradigms with respect to structural and physicochemical properties of hERG active and hERG inactive compounds are challenged. Finally, classification models with different data sets are trained. All source code is provided, which is based on the Python open source packages RDKit and scikit-learn to enable the community to rerun the experiments. The code is stored on github ( https://github.com/pzc/herg_chembl_jcim).
- Addresses
- Merck KGaA , Small Molecule Platform, Global Computational Chemistry, Frankfurter Strasse 250, 64293 Darmstadt, Germany.
- Autoren
- Paul Czodrowski
- DOI
- 10.1021/ci400308z
- eISSN
- 1549-960X
- Externe Identifier
- PubMed Identifier: 23944269
- Open access
- false
- ISSN
- 1549-9596
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- Journal of chemical information and modeling
- Schlüsselwörter
- Humans
- Computational Biology
- Software
- Databases, Protein
- Ether-A-Go-Go Potassium Channels
- Sprache
- eng
- Medium
- Print-Electronic
- Online publication date
- 2013
- Paginierung
- 2240 - 2251
- Datum der Veröffentlichung
- 2013
- Status
- Published
- Datum der Datenerfassung
- 2013
- Titel
- hERG me out.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 53
Datenquelle: Europe PubMed Central
- Abstract
- A detailed analysis of the hERG content inside the ChEMBL database is performed. The correlation between the outcome from binding assays and functional assays is probed. On the basis of descriptor distributions, design paradigms with respect to structural and physicochemical properties of hERG active and hERG inactive compounds are challenged. Finally, classification models with different data sets are trained. All source code is provided, which is based on the Python open source packages RDKit and scikit-learn to enable the community to rerun the experiments. The code is stored on github ( https://github.com/pzc/herg_chembl_jcim).
- Autoren
- Paul Czodrowski
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/23944269
- DOI
- 10.1021/ci400308z
- eISSN
- 1549-960X
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- J Chem Inf Model
- Schlüsselwörter
- Computational Biology
- Databases, Protein
- Ether-A-Go-Go Potassium Channels
- Humans
- Software
- Sprache
- eng
- Country
- United States
- Paginierung
- 2240 - 2251
- Datum der Veröffentlichung
- 2013
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2014
- Titel
- hERG me out.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 53
Datenquelle: PubMed
- Beziehungen:
-