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.
Discovery Technologies, Merck Serono Research, Merck KGaA , Frankfurter Straße 250, 64293 Darmstadt, Germany.
Paul
P
Czodrowski
Discovery Technologies, Merck Serono Research, Merck KGaA , Frankfurter Straße 250, 64293 Darmstadt, Germany.
Wolf-Guido
WG
Bolick
D000465:Algorithms; D000818:Animals; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D020407:Internet; D058990:Molecular Targeted Therapy; D064798:Polypharmacology; D011506:Proteins / Q000378:metabolism; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology; D012984:Software
D011506:Proteins; D054852:Small Molecule Libraries