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QSAR androgen receptor Data Set
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Abstract: 1024 binary attributes (molecular fingerprints) used to classify 1687 chemicals into 2 classes (binder to androgen receptor/positive, non-binder to androgen receptor /negative)

Data Set Characteristics:  

Multivariate

Number of Instances:

1687

Area:

Physical

Attribute Characteristics:

N/A

Number of Attributes:

1024

Date Donated

2019-10-01

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

3403


Source:

Francesca Grisoni (francesca.grisoni '@' unimib.it), Davide Ballabio (davide.ballabio '@' unimib.it), Viviana Consonni, Milano Chemometrics and QSAR Research Group (http://www.michem.unimib.it/), Università degli Studi Milano - Bicocca, Milano (Italy)


Data Set Information:

This dataset was used to develop classification QSAR models for the discrimination of binder/positive (199) and non-binder/negative (1488) molecules by means of different machine learning methods. Details can be found in the quoted reference: F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794.
Attributes (molecular fingerprints) were calculated at the Milano Chemometrics and QSAR Research Group (Università degli Studi Milano - Bicocca, Milano, Italy) on a set of chemicals provided by the National Center of Computational Toxicology, at the U.S. Environmental Protection Agency in the framework of the CoMPARA collaborative modelling project, which targeted the development of QSAR models to identify binders to the Androgen Receptor.


Attribute Information:

1024 binary molecular fingerprints and 1 experimental class:
1-1024) binary molecular fingerprint
1025) experimental class: positive (binder) and negative (non-binder)


Relevant Papers:

F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794



Citation Request:

Please, cite the following paper if you publish results based on the QSAR androgen receptor dataset: F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794


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