ExCAPE-DB : An integrated large scale dataset facilitating Big Data analysis in chemogenomics
(2017) In Journal of Cheminformatics 9(1).- Abstract
Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for... (More)
Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.
(Less)
- author
- publishing date
- 2017-03-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Big Data, Bioactivity, Chemical structure, Chemogenomics, Molecular fingerprints, QSAR, Search engine
- in
- Journal of Cheminformatics
- volume
- 9
- issue
- 1
- article number
- 17
- publisher
- ChemistryCentral
- external identifiers
-
- scopus:85014532240
- ISSN
- 1758-2946
- DOI
- 10.1186/s13321-017-0203-5
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2017 The Author(s).
- id
- b2485c69-9f7f-4937-9fd3-dca955058ede
- date added to LUP
- 2023-04-24 15:36:17
- date last changed
- 2023-04-27 07:35:33
@article{b2485c69-9f7f-4937-9fd3-dca955058ede, abstract = {{<p>Chemogenomics data generally refers to the activity data of chemical compounds on an array of protein targets and represents an important source of information for building in silico target prediction models. The increasing volume of chemogenomics data offers exciting opportunities to build models based on Big Data. Preparing a high quality data set is a vital step in realizing this goal and this work aims to compile such a comprehensive chemogenomics dataset. This dataset comprises over 70 million SAR data points from publicly available databases (PubChem and ChEMBL) including structure, target information and activity annotations. Our aspiration is to create a useful chemogenomics resource reflecting industry-scale data not only for building predictive models of in silico polypharmacology and off-target effects but also for the validation of cheminformatics approaches in general.</p>}}, author = {{Sun, Jiangming and Jeliazkova, Nina and Chupakin, Vladimir and Golib-Dzib, Jose Felipe and Engkvist, Ola and Carlsson, Lars and Wegner, Jörg and Ceulemans, Hugo and Georgiev, Ivan and Jeliazkov, Vedrin and Kochev, Nikolay and Ashby, Thomas J. and Chen, Hongming}}, issn = {{1758-2946}}, keywords = {{Big Data; Bioactivity; Chemical structure; Chemogenomics; Molecular fingerprints; QSAR; Search engine}}, language = {{eng}}, month = {{03}}, number = {{1}}, publisher = {{ChemistryCentral}}, series = {{Journal of Cheminformatics}}, title = {{ExCAPE-DB : An integrated large scale dataset facilitating Big Data analysis in chemogenomics}}, url = {{http://dx.doi.org/10.1186/s13321-017-0203-5}}, doi = {{10.1186/s13321-017-0203-5}}, volume = {{9}}, year = {{2017}}, }