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