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OCEAN: Optimized Cross rEActivity estimatioN
- Source :
- Journal of Chemical Information and Modeling. 56:2013-2023
- Publication Year :
- 2016
- Publisher :
- American Chemical Society (ACS), 2016.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Source code
Databases, Pharmaceutical
Polypharmacology
Computer science
General Chemical Engineering
media_common.quotation_subject
Library and Information Sciences
computer.software_genre
Small Molecule Libraries
03 medical and health sciences
Drug Discovery
Animals
Humans
Molecular Targeted Therapy
media_common
Internet
Drug discovery
Proteins
General Chemistry
chEMBL
Computer Science Applications
030104 developmental biology
Molecular targets
Data mining
computer
Algorithms
Software
Subjects
Details
- ISSN :
- 1549960X and 15499596
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Information and Modeling
- Accession number :
- edsair.doi.dedup.....eece36ec663498ab622b7c1b0656fd37