Data
qsbr_y2

qsbr_y2

active ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
0 likes downloaded by 1 people , 1 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Date unknown Please cite: This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode). The molecules and outputs are taken from the original studies (see below). The other datasets are taken exactly from the original studies. The last attribute in each file is the target. Original studies: carbolenes "B. D. Silverman and Daniel. E. Platt, J. Med. Chem. 1996, 39, 2129-2140" mtp2 "Bergstrom, C. A. S.; Norinder, U.; Luthman, K.; Artursson, P. Molecular Descriptors Influencing Melting Point and Their Role in Classification of Solid Drugs. J. Chem. Inf. Comput. Sci.; (Article); 2003; 43(4); 1177-1185" chang, cristalli, depreux, doherty, garrat2, garrat, heyl, krystek, lewis, penning, rosowsky, siddiqi, stevenson, strupcz, svensson, thompson, tsutumi, uejling, yokoyama1, yokoyama2 "David E Patterson, Richard D Cramer, Allan M Ferguson, Robert D Clark, Laurence W Weinberger. Neighbourhood Behaviour: A Useful Concept for Validation of ""Molecular Diversity"" Descriptors. J. Med. Chem. 1996 (39) 3049 - 3059." mtp "Karthikeyan, M.; Glen, R.C.; Bender, A. General melting point prediction based on a diverse compound dataset and artificial neural networks. J. Chem. Inf. Model.; 2005; 45(3); 581-590" benzo32 "Harrison,P.W. and Barlin,G.B. and Davies,L.P. and Ireland,S.J. and Matyus,P. and Wong,M.G., Syntheses, pharmacological evaluation and molecular modelling of substituted 6-alkoxyimidazo[1,2-b]pyridazines as new ligands for the benzodiazepine receptor, European Journal of Medicinal Chemistry, (31), 1996, 651-662" PHENETYL1 "H. Kubinyi (Ed.): ""QSAR: Hansch Analysis and Related Approaches"", VCH, Weinhein (Ger), 1993, pp.57-68" pah "Todeschini, R.; Gramatica, P.; Marengo, E.; Provenzani, R. Weighted Holistic Invariant Molecular Descriptors. Part 2. Theory Development and Applications on Modeling Physico-Chemical Properties of PolyAromatic Hydrocarbons (PAH). Chemom. Intell. Lab. Syst. 1995, 27, 221-229." pdgfr "R. Guha and P. Jurs. The Development of Linear, Ensemble and Non-linear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. J. Chem. Inf. Comput. Sci. 2004, 44 (6), 2179-2189" Phen "Cammarata, A. Interrelationship of the Regression Models Used for Structure-Activity Analyses. J. Med. Chem. 1972, 15, 573-577" topo_2_1, yprop_4_1 "Jun Feng et al, Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods, J. Chem. Inf Comput. Sci., 2003 (43) 1463-1470" qsabr1, qsabr2 "Damborsky, J., Schultz, T.W., Comparison of the QSAR models for toxicity and biodegradability of anilines and phenols, Chemosphere 34: 429-446, 1997" qsartox "Blaha, L., Damborsky, J., Nemec, M., QSAR for acute toxicity of saturated and unsaturated halogenated aliphatic compounds, Chemosphere 36: 1345-1365, 1998" qsbr_rw1 "Damborsky, J. et al., Structure-biodegradability relationships for chlorinated dibenzo-p-dioxins and dibenzofurans, In: Wittich, R.-M., Biodegradation of dioxins and furans, R.G. Landes Company, Austin, 1998" qsbr_y2 "Damborsky, J. et al., A mechanistic approach to deriving QSBR- A case study: dehalogenation of haloaliphatic compounds, In: Peijnenburg, W.J.G.M., Damborsky, J., Biodegradability Prediction, Kluwer Academic Publishers" qsbralks "Damborsky, J. et al., Mechanism-based Quantitative Structure-Biodegradability Relationships for hydrolytic dehalogenation of chloro- and bromo-alkenes, Quantitative Structure-Activity Relationships 17: 450-458, 1998" qsfrdhla "Damborsky, J., Quantitative structure-function relationships of the single-point mutants of haloalkane dehalogenase: A multivariate approach, Qunatitative Structure-Activity Relationships 16: 126-135, 1997" qsfsr1 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsfsr2 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsprcmpx "Cajan, M. et al., Stability of Aromatic Amides with Bromide Anion: Quantitative Structure-Property Relationships, Journal of Chemical Information and Computer Sciences, in press, 2000" selwood "Selwood, D. L.; Livingstone, D. J.; Comley, J. C.; O'Dowd, A. B.; Hudson, A. T.; Jackson, P.; Jandu, K. S.; Rose, V. S.; Stables, J. N. Structure-Activity Relationships of Antifilarial Antimycin Analogues: A Multivariate Pattern Recognition Study J. Med. Chem., 1990, 33, 136-142"

10 features

oz10 (target)numeric24 unique values
0 missing
oz1numeric25 unique values
0 missing
oz2numeric25 unique values
0 missing
oz3numeric23 unique values
0 missing
oz4numeric25 unique values
0 missing
oz5numeric25 unique values
0 missing
oz6numeric25 unique values
0 missing
oz7numeric21 unique values
0 missing
oz8numeric19 unique values
0 missing
oz9numeric6 unique values
0 missing

19 properties

25
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
10
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.82
Average class difference between consecutive instances.
0
Percentage of missing values.
0.4
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.

13 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz10
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz10
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task