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QSAR oral toxicity Data Set
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Abstract: Data set containing values for 1024 binary attributes (molecular fingerprints) used to classify 8992 chemicals into 2 classes (very toxic/positive, not very toxic/negative)

Data Set Characteristics:  

Multivariate

Number of Instances:

8992

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:

5209


Source:

Davide Ballabio (davide.ballabio '@' unimib.it), Francesca Grisoni, Roberto Todeschini, 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 very toxic/positive (741) and not very toxic/negative (8251) molecules by means of different machine learning methods. Details can be found in the quoted reference: D. Ballabio, F. Grisoni, V. Consonni, R. Todeschini (2019), Integrated QSAR models to predict acute oral systemic toxicity, Molecular Informatics, 38, 180012; doi: 10.1002/minf.201800124.
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 ICCVAM Acute Toxicity Workgroup (U.S. Department of Health and Human Services), in collaboration with the U.S. Environmental Protection Agency (U.S. EPA, National Center for Computational Toxicology), which coordinated the “Predictive Models for Acute Oral Systemic Toxicity” collaborative project to develop in silico models to predict acute oral systemic toxicity for filling regulatory needs.


Attribute Information:

1024 binary molecular fingerprints and 1 experimental class:
1-1024) binary molecular fingerprint
1025) experimental class: positive (very toxic) and negative (not very toxic)


Relevant Papers:

D. Ballabio, F. Grisoni, V. Consonni, R. Todeschini (2019), Integrated QSAR models to predict acute oral systemic toxicity, Molecular Informatics, 38, 180012; doi: 10.1002/minf.201800124



Citation Request:

Please, cite the following paper if you publish results based on the QSAR oral toxicity dataset: D. Ballabio, F. Grisoni, V. Consonni, R. Todeschini (2019), Integrated QSAR models to predict acute oral systemic toxicity, Molecular Informatics, 38, 180012; doi: 10.1002/minf.201800124


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