QSAR biodegradation
Donated on 6/20/2013
Data set containing values for 41 attributes (molecular descriptors) used to classify 1055 chemicals into 2 classes (ready and not ready biodegradable).
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
1055
# Features
-
Dataset Information
Additional Information
The QSAR biodegradation dataset was built in the Milano Chemometrics and QSAR Research Group (Università degli Studi Milano – Bicocca, Milano, Italy). The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement n. 238701 of Marie Curie ITN Environmental Chemoinformatics (ECO) project. The data have been used to develop QSAR (Quantitative Structure Activity Relationships) models for the study of the relationships between chemical structure and biodegradation of molecules. Biodegradation experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE). Classification models were developed in order to discriminate ready (356) and not ready (699) biodegradable molecules by means of three different modelling methods: k Nearest Neighbours, Partial Least Squares Discriminant Analysis and Support Vector Machines. Details on attributes (molecular descriptors) selected in each model can be found in the quoted reference: Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V. (2013). Quantitative Structure - Activity Relationship models for ready biodegradability of chemicals. Journal of Chemical Information and Modeling, 53, 867-878.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
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0 to 10 of 41
Additional Variable Information
41 molecular descriptors and 1 experimental class: 1) SpMax_L: Leading eigenvalue from Laplace matrix 2) J_Dz(e): Balaban-like index from Barysz matrix weighted by Sanderson electronegativity 3) nHM: Number of heavy atoms 4) F01[N-N]: Frequency of N-N at topological distance 1 5) F04[C-N]: Frequency of C-N at topological distance 4 6) NssssC: Number of atoms of type ssssC 7) nCb-: Number of substituted benzene C(sp2) 8) C%: Percentage of C atoms 9) nCp: Number of terminal primary C(sp3) 10) nO: Number of oxygen atoms 11) F03[C-N]: Frequency of C-N at topological distance 3 12) SdssC: Sum of dssC E-states 13) HyWi_B(m): Hyper-Wiener-like index (log function) from Burden matrix weighted by mass 14) LOC: Lopping centric index 15) SM6_L: Spectral moment of order 6 from Laplace matrix 16) F03[C-O]: Frequency of C - O at topological distance 3 17) Me: Mean atomic Sanderson electronegativity (scaled on Carbon atom) 18) Mi: Mean first ionization potential (scaled on Carbon atom) 19) nN-N: Number of N hydrazines 20) nArNO2: Number of nitro groups (aromatic) 21) nCRX3: Number of CRX3 22) SpPosA_B(p): Normalized spectral positive sum from Burden matrix weighted by polarizability 23) nCIR: Number of circuits 24) B01[C-Br]: Presence/absence of C - Br at topological distance 1 25) B03[C-Cl]: Presence/absence of C - Cl at topological distance 3 26) N-073: Ar2NH / Ar3N / Ar2N-Al / R..N..R 27) SpMax_A: Leading eigenvalue from adjacency matrix (Lovasz-Pelikan index) 28) Psi_i_1d: Intrinsic state pseudoconnectivity index - type 1d 29) B04[C-Br]: Presence/absence of C - Br at topological distance 4 30) SdO: Sum of dO E-states 31) TI2_L: Second Mohar index from Laplace matrix 32) nCrt: Number of ring tertiary C(sp3) 33) C-026: R--CX--R 34) F02[C-N]: Frequency of C - N at topological distance 2 35) nHDon: Number of donor atoms for H-bonds (N and O) 36) SpMax_B(m): Leading eigenvalue from Burden matrix weighted by mass 37) Psi_i_A: Intrinsic state pseudoconnectivity index - type S average 38) nN: Number of Nitrogen atoms 39) SM6_B(m): Spectral moment of order 6 from Burden matrix weighted by mass 40) nArCOOR: Number of esters (aromatic) 41) nX: Number of halogen atoms 42) experimental class: ready biodegradable (RB) and not ready biodegradable (NRB)
Dataset Files
| File | Size |
|---|---|
| biodeg.csv | 152.3 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset qsar_biodegradation = fetch_ucirepo(id=254) # data (as pandas dataframes) X = qsar_biodegradation.data.features y = qsar_biodegradation.data.targets # metadata print(qsar_biodegradation.metadata) # variable information print(qsar_biodegradation.variables)
Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., & Consonni, V. (2013). QSAR biodegradation [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5H60M.
Creators
Kamel Mansouri
Tine Ringsted
Davide Ballabio
Roberto Todeschini
Viviana Consonni
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.