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Open AccessReview

How Open Data Shapes In Silico Transporter Modeling

Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, A-1090 Vienna, Austria
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Author to whom correspondence should be addressed.
Academic Editor: Maria Emília de Sousa
Molecules 2017, 22(3), 422; https://doi.org/10.3390/molecules22030422
Received: 2 February 2017 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 7 March 2017
(This article belongs to the Special Issue Can Membrane Transporters Contribute to Drug Discovery?)
Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine. View Full-Text
Keywords: transport proteins; computational modeling; open data; data curation; machine learning; multi-label classification; applicability domain transport proteins; computational modeling; open data; data curation; machine learning; multi-label classification; applicability domain
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MDPI and ACS Style

Montanari, F.; Zdrazil, B. How Open Data Shapes In Silico Transporter Modeling. Molecules 2017, 22, 422. https://doi.org/10.3390/molecules22030422

AMA Style

Montanari F, Zdrazil B. How Open Data Shapes In Silico Transporter Modeling. Molecules. 2017; 22(3):422. https://doi.org/10.3390/molecules22030422

Chicago/Turabian Style

Montanari, Floriane; Zdrazil, Barbara. 2017. "How Open Data Shapes In Silico Transporter Modeling" Molecules 22, no. 3: 422. https://doi.org/10.3390/molecules22030422

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