AZOrange - High performance Open Source machine learning for QSAR modeling in a graphical programming environment

Journal of Cheminformatics

Volumn 3, Issue 7, 2011, Pages

  Stålring, Jonna C ^a   Carlsson, Lars A ^a   Almeida, Pedro ^b   Boyer, Scott ^a  

^a ASTRAZENECA R AND D   (Sweden)

^b Avenida Infante D Henrique n 145   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84862653390     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-3-28     Document Type: Article

References (24)

1. Organisation for Economic Co-operation and Development: The report from the expert group on (quantitative) structure-activity relationships [(Q) SARs] on the principles for the validation of (Q) SARs. OECD series on testing and assessment No. 49. ENV/JM/MONO (2004) 24 OECD, Paris, France.
2. van de Waterbeemed H, Gifford E: ADMET in silico modelling: towards predictin paradise? Nature Reviews Drug Discovery 2003, 2:192-204. (Pubitemid 37361664)
3. O'Brien S, de Groot MJ: Greater Than the Sum of its Parts: Combining Models for ADMET Prediction. J Med Chem 2005, 48:1287-91. (Pubitemid 40270476)
4. Bruce C: Contemporary QSAR Classifiers Compared. J Chem Inf Model 2007, 47:219-27.
5. Dudek AZ, Arodz T, Galvez J: Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review. Combinatorial Chemistry and High Throughtput Screening 2006, 9:213-28.
6. Gola J, Obrezanove O, Champness E, Segall M: ADMET Property Prediction: The State of the Art and Current Challenges. QSAR and Combinatorial Science 2006, 25:1172-1180. (Pubitemid 46052454)
7. R: A language and environment for statistical computing 2010 [http://www.rproject.org].
8. Demsar J, Zupan B, Leban G, Curk T: Orange: From Experimental Machine Learning to Interactive Data Mining. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases New York, NY, USA: Springer-Verlag; 2004, 537-539. (Pubitemid 39740441)
9. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA Data Mining Software: An Update. SIGKDD Explorations 2009, 11:10-18.
10. KNIMEtech. [http://tech.knime.org/].
11. Chang CC, Lin CJ: LIBSVM: a library for support vector machines 2001 [http://www.csie.ntu.edu.tw/~cjlin/libsvm].
12. OpenCV. [http://opencv. willowgarage.com/wiki/].
13. PLearn. [http://plearn. berlios.de/].
14. Gray GA, Kolda TG: Algorithm 856: APPSPACK 4.0: Asynchronous parallel pattern search for derivative-free optimization. ACM Transactions on Mathematical Software 2006, 32:485-507. (Pubitemid 44495252)
15. O'Boyle NM, Hutchison GR: Cinfony - combining Open Source cheminformatics toolkits behind a common interface. Chemistry Central Journal 2008, 2:24.
16. Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E: The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics. J Chem Inf Comput Sci 2003, 43:493-500.
17. Landrum G: RDKit [http://www.rdkit.org].
18. OpenBabel. [http://openbabel.org].
19. Breiman L: Looking inside the black box 2002 [http://www.stat.berkeley. edu/~breiman/wald2002-2.pdf].
20. Breiman L: Random Forests. Machine Learning 2001, 45:5-32. (Pubitemid 32933532)
21. Prechelt L: Automatic early stopping using cross validation: quantifying the criteria. Neural Networks 1998, 11:761-767. (Pubitemid 28400276)
22. Beale MH, Demuth HB, Hagan MT: Neural network design Boulder, CO: University of Colorado Bookstore; 2002.
23. Cawley G, Talbot N: Preventing Over-Fitting during Modeul Selection via Bayesian Regularisation of the Hyper-Parameters. J of Machine Learning Research 2007, 8:841-861. (Pubitemid 46677044)
24. Akaike H: Information theory and an extension of the maximum likelihood principle. In Second international symposium of information theory. Volume 1. Budapest, Hungary: Akademiai Kiado; 1973:267-281.