%0 Online Multimedia %A Czodrowski, Paul %A Bolick, Wolf-Guido %D 2016 %T OCEAN: Optimized Cross rEActivity estimatioN %U https://acs.figshare.com/articles/media/OCEAN_Optimized_Cross_rEActivity_estimatioN/3859530 %R 10.1021/acs.jcim.6b00067.s008 %2 https://acs.figshare.com/ndownloader/files/6054639 %K success rate %K OCEAN performance check %K success rates %K Optimized Cross rEActivity estimatioN %K ChEMBL 20 compounds %K ChEMBL data %K phenotypic screens %K source code %K ChEMBL 21 compounds %K drug discovery process %K heuristics approach %K New ChEMBL data %K TOP 10 ranks %K off-target elucidation %K target prediction tool %K polypharmacological compounds %K ChEMBL 20 %X 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 %I ACS Publications