Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

QSAR Bioconcentration classes dataset Data Set
Download: Data Folder, Data Set Description

Abstract: Dataset of manually-curated Bioconcentration factor (BCF, fish) and mechanistic classes for QSAR modeling.

Data Set Characteristics:  

Multivariate

Number of Instances:

779

Area:

Life

Attribute Characteristics:

N/A

Number of Attributes:

14

Date Donated

2019-10-11

Associated Tasks:

Classification, Regression

Missing Values?

N/A

Number of Web Hits:

4653


Source:

Francesca Grisoni (francesca.grisoni '@' unimib.it), Viviana Consonni (viviana.consonni '@' unimib.it), Marco Vighi, Sara Villa, Roberto Todeschini


Data Set Information:

A dataset of manually-curated BCF for 779 chemicals was used to determine the mechanisms of bioconcentration, i.e. to predict whether a chemical: (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g. proteins), or (3) is metabolized/eliminated. Data were randomly split into a training set of 584 compounds (75%) and a test set of 195 compounds (25%), preserving the proportion between the classes. Two QSAR classification trees were developed using CART (Classification and Regression Trees) machine learning technique coupled with Genetic Algorithms. The file contains the selected Dragon descriptors (9) along with CAS, SMILES, experimental BCF, experimental/predicted KOW and mechanistic class (1, 2, 3). Further details on model development and performance along with descriptor definitions and interpretation are provided in the original manuscript (Grisoni et al., 2016).


Attribute Information:

3 Compound identifiers:
- CAS number
- Molecular SMILES
- Train/test splitting

9 molecular descriptors (independent variables)
- nHM
- piPC09
- PCD
- X2Av
- MLOGP
- ON1V
- N-072
- B02[C-N]
- F04[C-O]

2 experimental responses:
- Bioconcentration Factor (BCF) in log units (regression)
- Bioaccumulation class (three classes)


Relevant Papers:

F. Grisoni, V.Consonni, M.Vighi, S.Villa, R.Todeschini (2016). Investigating the mechanisms of bioconcentration through QSAR classification trees, Environment International, 88, 198-205



Citation Request:

The dataset is freeware and may be used if proper reference is given to the authors. Please, refer to the following papers:
F. Grisoni, V.Consonni, M.Vighi, S.Villa, R.Todeschini (2016). Investigating the mechanisms of bioconcentration through QSAR classification trees, Environment International, 88, 198-205.
F. Grisoni, V. Consonni, S. Villa, M. Vighi, R. Todeschini (2015). QSAR models for bioconcentration: Is the increase in the complexity justified by more accurate predictions?. Chemosphere, 127, 171-179.


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML