10.1021/acs.jcim.6b00067.s002 Paul Czodrowski Paul Czodrowski Wolf-Guido Bolick Wolf-Guido Bolick OCEAN: Optimized Cross rEActivity estimatioN American Chemical Society 2016 success rate OCEAN performance check success rates Optimized Cross rEActivity estimatioN ChEMBL 20 compounds ChEMBL data phenotypic screens source code ChEMBL 21 compounds drug discovery process heuristics approach New ChEMBL data TOP 10 ranks off-target elucidation target prediction tool polypharmacological compounds ChEMBL 20 2016-09-26 17:22:10 Media https://acs.figshare.com/articles/media/OCEAN_Optimized_Cross_rEActivity_estimatioN/3859533 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