References
- Fermini , B and Fossa , A . 2003 . The impact of drug-induced QT interval prolongation on drug discovery and development . Nature Rev. Drug Discov. , 2 : 439 – 447 .
- Haverkamp , W . 2000 . The potential for QT prolongation and proarrhythmia by non-antiarrhythmic drugs: Clinical and regulatory implications. Report on a policy conference of the European Society of Cardiology . Eur. Heart J. , 21 : 1216 – 1231 .
- Recanatini , M , Poluzzi , E , Masetti , M , Cavalli , A and De Ponti , F . 2005 . QT prolongation through hERG K+ channel blockade: Current knowledge and strategies for the early prediction during drug development . Med. Res. Rev , 25 : 133 – 166 .
- Chekmarev , DS , Kholodovich , V , Balakin , KV , Ivanenkov , Y , Ekins , S and Welsh , WJ . 2008 . Shape signatures: New descriptors for predicting cardiotoxicity in silico . Chem. Res. Toxicol. , 21 : 1304 – 1314 .
- Li , Q , Jorgensen , FS , Oprea , T , Brunak , S and Taboreau , O . 2008 . HERG Classification model based on a combination of support vector machine method and GRIND descriptors . Mol. Pharmaceutics , 5 : 117 – 127 .
- Nisius , B and Goller , A . 2009 . Similarity-based classifier using topomers to provide a knowledge base for hERG channel inhibition . J. Chem. Inf. Model. , 49 : 247 – 256 .
- Tobita , M , Nishikawa , T and Nagashima , R . 2005 . A discriminant model constructed by the support vector maachine method for hERG potassium channel inhibitors . Bioorg. Med. Chem. Lett. , 15 : 2886 – 2890 .
- Yap , CW , Cai , CZ , Xue , Y and Chen , YZ . 2004 . Prediction of torsade-causing potential of drugs by support vector machine approach . Toxicol. Sci. , 79 : 170 – 177 .
- Obrezanova , O and Segall , MD . 2010 . Gaussian processes for classification: QSAR modeling of ADMET and target activity . J. Chem. Inf. Model. , 50 : 1053 – 1061 .
- Doddareddy , M , Klaase , EC , Shugufta , Izerman , AP and Bender , AP . 2010 . Prospective validation of a comprehensive in silico HERG model and its applications to commercial compound and drug database . ChemMedChem , 5 : 716 – 729 .
- Roche , O , Trube , G , Zuegge , J , Pflimlin , P , Alanine , A and Schneider , G . 2002 . A virtual screening method for prediction of the HERG potassium channel liability of compound libraries . ChemBioChem , 3 : 455 – 459 .
- Thai , K-M and Ecker , GF . 2008 . Classification models for HERG inhibitors by counter-propagation neural networks . ChemBiolDrugDes , 72 : 279 – 289 .
- Thai , K-M and Ecker , GF . 2009 . Similarity-based SIBAR descriptors for classification of chemically diverse HERG blockers . Mol. Divers. , 13 : 321 – 336 .
- Polak , S , Wisniowska , B , Ahamadi , M and Mendyk , A . 2011 . Prediction of the HERG potassium channel inhibition potential with use of artificial neural network . Appl. Soft Comput. , 11 : 2611 – 2617 .
- Sinha , N and Sen , S . 2011 . Predicting HERG activities of compounds from their 3D structures: Development and evaluation of a global descriptors based QSAR model . Eur. J. Med. Chem. , 46 : 618 – 630 .
- Sun , H . 2006 . An accurate and interpretable bayesian classification model for predicting of HERG liability . ChemMedChem , 1 : 315 – 322 .
- Dubus , E , Ijjaali , I , Petitet , F and Michel , A . 2006 . In silico classification of hERG channel blockers: A knowledge-based strategy . ChemMedChem , 1 : 622 – 630 .
- Gepp , MM and Hutter , MC . 2006 . Determination of hERG channel blockers using a decision tree . Bioorg. Med. Chem. , 14 : 5325 – 5332 .
- Ekins , S , Balakin , KV , Savchuk , N and Ivanenkov , Y . 2006 . Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitionning and Kohonen ana Sammon mapping techniques . J. Med. Chem. , 49 : 5059 – 5071 .
- Kireeva , N , Baskin , II , Gaspar , HA , Horvath , D , Marcou , G and Varnek , A . 2012 . Generative topographic maps (GTM): Universal tool for data visualization, structure-activity modeling and database comparison . Mol. Inf. , 31 : 301 – 312 .
- Jolliffe , IT . 2002 . Principal Component Analysis , 2nd ed. , New York : Springer .
- Sammon , JW . 1969 . A nonlinear mapping for data structure analysis . IEEE Trans. Comput. , 18 : 401 – 409 .
- Kohonen , T , Schröder , MR and Huang , T.S. (eds.) . 2001 . Self-Organizing Maps , New York : Springer .
- Balakin , K.V. (ed.) . 2010 . Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery , Hoboken , NJ : Wiley .
- Maniyar , DM , Nabney , IT , Williams , BS and Sewing , A . 2006 . Data visualization during the early stages of drug discovery . J. Chem. Inf. Model. , 46 : 1806 – 1818 .
- Yin , H . 2003 . Nonlinear Multidimensional Data Projection and Visualization, in Intelligent Data Engineering and Automated Learning , Edited by: Liu , J. , Cheung , Y. and Yin , H. 377 – 388 . Heidelberg : Springer-Verlag .
- Bishop , CM and Svensen , M . 1998 . GTM: The generative topographic mapping . Neural Comput. , 10 : 215 – 234 .
- Bishop , CM , Svensen , M and Williams , CLI . 1997 . GTM: A principled alternative to the self-organizing map , Neural Computing Research Group : Technical Report .
- M. Svensen, GTM: The generative topographic mapping, PhD thesis, Aston University, 1998
- Nisius , B , Gцller , AH and Bajorath , J . 2009 . Combining cluster analysis, feature selection and multiple support vector machine models for the identification of human ether-a-go-go related gene channel blocking compounds . Chem. Biol. Drug Des. , 73 : 17 – 25 .
- Chemaxon Standardizer; software available at http://www.chemaxon.com/library/scientific-presentations/standardizer/
- Instant JChem, software available at http://www.chemaxon.com/products/instant-jchem/
- Varnek , A , Fourches , D , Horvath , D , Klimchuk , O , Gaudin , C , Vayer , P , Solov’ev , V , Hoonakker , F , Tetko , IV and Marcou , G . 2008 . ISIDA – Platform for virtual screening based on fragment and pharmacophoric descriptors . Curr. Comp.-Aid. Drug Des. , 4 : 191 – 198 .
- Swamy , MNS and Thulasiraman , K . 1981 . Graphs, Networks, and Algorithms , New York : John Wiley & Sons .
- Ruggiu , F , Marcou , G , Varnek , A and Horvath , D . 2010 . ISIDA property-labelled fragment descriptors . Mol. Inform. , 29 : 855 – 868 .
- Guha , R . 2007 . Chemical informatics functionality in R . J. Stat. Software , 18 : 1 – 16 .
- R project, software available at http://www.r-project.org/foundation/
- Cristianini , N and Shawe-Taylor , J . 2000 . “ An Introduction to Support Vector Machines (and Other Kernel-Based Learning Methods) ” . In Cambridge Monographs on Applied and Computational Mathematics , Cambridge : Cambridge University Press .
- Ivanciuc , O . 2007 . Applications of Support Vector Machines in Chemistry , Weinheim : Wiley-VCH .
- Vapnik , V . 1998 . Statistical Learning Theory , New York and Chichester : Wiley-Interscience .
- Vapnik , VN . 1995 . The Nature of Statistical Learning Theory , New York : Springer .
- Williams , CLI , Revow , M and Hinton , G . 1996 . Instantiating deformable models with a neural net . Comp. Vision Image Underst. , 68 : 120 – 126 .
- Sokolova , M , Japkowicz , N and Szpakowicz , S . 2006 . Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation . Adv. Artif. Intell. , 4304 : 1015 – 1021 .
- C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
- Smola , AJ and Schölkopf , B . 2004 . A Tutorial on support vector regression . Stat. Comput. , 14 : 199 – 222 .
- Y.-W. Chang and C.-J. Lin, Feature Ranking Using Linear SVM, JMLR: Workshop and Conference Proceedings, Vol. 3, 2008, pp. 53–64
- Netlab, www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/
- Nabney , I . 2002 . Algorithms for Pattern Recognition , London : Springer .
- Bishop , CM . 2006 . Pattern Recognition and Machine Learning, Information Science and Statistics , New York : Springer .
- Golbraikh , A and Tropsha , A . 2002 . Beware of q2! . J. Mol. Graphics Model. , 20 : 269 – 276 .
- Baskin , II , Kireeva , N and Varnek , A . 2010 . The one-class classification approach to data description and to models applicability domain . Mol. Inf. , 29 : 581 – 587 .
- Soto , AJ , Vazquez , GE , Strickert , M and Ponzoni , I . 2011 . Target-driven subspace mapping methods and their applicability domain estimation . Mol. Inf. , 30 : 779 – 789 .