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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Andreas Mayr^{a b c d}, Günter Klambauer^{a b c d}, Thomas Unterthiner^{a b c d}, Marvin N. Steijaert^{e f g}, Jörg K. Wegner^{e h i}, Hugo Ceulemans^{e h i}, Djork-Arné Clevert^{j k l}, Sepp Hochreiter^{a b c d}

^aAustria
^bJohannes Kepler University Linz
^cLIT AI Lab and Institute of Bioinformatics
^dLIT AI Lab and Institute of Bioinformatics, Johannes Kepler University Linz, Austria
^eBelgium
^fOpen Analytics NV
^gOpen Analytics NV, Belgium
^hJanssen Pharmaceutica NV
ⁱJanssen Pharmaceutica NV, Belgium
^jBayer AG
^kGermany
^lBayer AG, Germany

Chemical Science Royal Society of Chemistry2018-01-01 581 citations65 references

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Abstract

The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors. The results are based on a benchmark of 1300 assays and half a million compounds.

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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

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