SwissTargetPrediction: a web server for target prediction of bioactive small molecules

By David Gfeller, Aurélien Grosdidier, M. Wirth, Antoine Daina, Olivier Michielin, Vincent Zoete. Nucleic Acids Res., 2014

1,594 citations recorded by the source corpus.

Bioactive small molecules, such as drugs or metabolites, bind to proteins or other macro-molecular targets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mechanisms underlying their bioactivity and predicting potential side effects or cross-reactivity. Recently, large datasets of protein–small molecule interactions have become available, providing a unique source of information for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTargetPrediction, a web server to accurately predict the targets of bioactive molecules based on a combination of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five different organisms, and mapping predictions by homology within and between different species is enabled for close paralogs and orthologs. SwissTargetPrediction is accessible free of charge and without login requirement at http://www.swisstargetprediction.ch.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/ce42220ede5bfc4a84e484d519b58b7c72a314e5

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Citations in the corpus

The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015, methodology): “This automated curation process was applied meticulously during the development of the reverse screening tool SwissTar-getPrediction (www.swisstargetprediction.ch) (66–68) and for the future ligand-based screening tool SwissSimilarity (www.swisssimilarity.ch).”
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016, methodology): “…to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules such as SwissTargetPrediction [34], in general, the available methods remain too inaccurate for systematic binding predictions and physical experiments remain the state of the art…”
SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules (2019, background): “As mentioned earlier and described in details elsewhere (12), the probability values, depicted here as green bars, are calculated from the Combined-Scores of the most similar compounds to the query molecule (in 2D and 3D) known to be active on a given protein.”