dc.contributor.author
Banerjee, Priyanka
dc.contributor.author
Siramshetty, Vishal B.
dc.contributor.author
Drwal, Malgorzata N.
dc.contributor.author
Preissner, Robert
dc.date.accessioned
2018-06-08T04:22:18Z
dc.date.available
2016-10-31T12:19:28.768Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/17158
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-21338
dc.description.abstract
Background With a constant increase in the number of new chemicals synthesized
every year, it becomes important to employ the most reliable and fast in
silico screening methods to predict their safety and activity profiles. In
recent years, in silico prediction methods received great attention in an
attempt to reduce animal experiments for the evaluation of various
toxicological endpoints, complementing the theme of replace, reduce and
refine. Various computational approaches have been proposed for the prediction
of compound toxicity ranging from quantitative structure activity relationship
modeling to molecular similarity-based methods and machine learning. Within
the “Toxicology in the 21st Century” screening initiative, a crowd-sourcing
platform was established for the development and validation of computational
models to predict the interference of chemical compounds with nuclear receptor
and stress response pathways based on a training set containing more than
10,000 compounds tested in high-throughput screening assays. Results Here, we
present the results of various molecular similarity-based and machine-learning
based methods over an independent evaluation set containing 647 compounds as
provided by the Tox21 Data Challenge 2014. It was observed that the Random
Forest approach based on MACCS molecular fingerprints and a subset of 13
molecular descriptors selected based on statistical and literature analysis
performed best in terms of the area under the receiver operating
characteristic curve values. Further, we compared the individual and combined
performance of different methods. In retrospect, we also discuss the reasons
behind the superior performance of an ensemble approach, combining a
similarity search method with the Random Forest algorithm, compared to
individual methods while explaining the intrinsic limitations of the latter.
Conclusions Our results suggest that, although prediction methods were
optimized individually for each modelled target, an ensemble of similarity and
machine-learning approaches provides promising performance indicating its
broad applicability in toxicity prediction.
en
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Similarity searching
dc.subject
Machine learning
dc.subject
Toxicity prediction
dc.subject
Tox21 challenge
dc.subject
Molecular fingerprints
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit
dc.title
Computational methods for prediction of in vitro effects of new chemical
structures
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Journal of Cheminformatics. - 8 (2016), Artikel Nr. 51
dcterms.bibliographicCitation.doi
10.1186/s13321-016-0162-2
dcterms.bibliographicCitation.url
http://jcheminf.springeropen.com/articles/10.1186/s13321-016-0162-2
refubium.affiliation
Charité - Universitätsmedizin Berlin
de
refubium.mycore.fudocsId
FUDOCS_document_000000025627
refubium.note.author
Der Artikel wurde in einer reinen Open-Access-Zeitschrift publiziert.
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000007270
dcterms.accessRights.openaire
open access