OCEAN: Optimized Cross rEActivity estimatioN
The prediction of molecular targets is highly beneficial during the drug discovery process, be it for off-target elucidation or deconvolution of phenotypic screens. Here, we present OCEAN, a target prediction tool exclusively utilizing publically available ChEMBL data. OCEAN uses a heuristics approach based on a validation set containing almost 1000 drug ← → target relationships. New ChEMBL data (ChEMBL20 as well as ChEMBL21) released after the validation was used for a prospective OCEAN performance check. The success rates of OCEAN to predict correctly the targets within the TOP10 ranks are 77% for recently marketed drugs and 62% for all new ChEMBL20 compounds and 51% for all new ChEMBL21 compounds. OCEAN is also capable of identifying polypharmacological compounds; the success rate for molecules simultaneously hitting at least two targets is 64% to be correctly predicted within the TOP10 ranks. The source code of OCEAN can be found at http://www.github.com/rdkit/OCEAN
Paul Czodrowski* and Wolf-Guido Bolick
Discovery Technologies, Merck Serono Research, Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
J. Chem. Inf. Model., Article ASAP
Publication Date (Web): September 26, 2016
Copyright © 2016 American Chemical Society
*P. Czodrowski. E-mail: firstname.lastname@example.org.