Self‐Organizing Maps for In Silico Screening and Data Visualization
暂无分享,去创建一个
[1] M. V. Velzen,et al. Self-organizing maps , 2007 .
[2] Tudor I. Oprea,et al. Virtual screening applications: a study of ligand-based methods and different structure representations in four different scenarios , 2007, J. Comput. Aided Mol. Des..
[3] Teuvo Kohonen,et al. Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.
[4] Joost N. Kok,et al. TreeSOM: Cluster analysis in the self-organizing map , 2006, Neural Networks.
[5] Michael Schmuker,et al. SOMMER: self-organising maps for education and research , 2006, Journal of molecular modeling.
[6] R. Brereton,et al. Supervised self organizing maps for classification and determination of potentially discriminatory variables: illustrated by application to nuclear magnetic resonance metabolomic profiling. , 2010, Analytical chemistry.
[7] G. Schneider,et al. Scaffold architecture and pharmacophoric properties of natural products and trade drugs: application in the design of natural product-based combinatorial libraries. , 2001, Journal of combinatorial chemistry.
[8] Igor V. Tetko,et al. CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals , 2011, Molecular informatics.
[9] E. Sausville,et al. Mining the National Cancer Institute's tumor-screening database: identification of compounds with similar cellular activities. , 2002, Journal of medicinal chemistry.
[10] Rebecca Harris,et al. Supervised Self-Organizing Maps in Drug Discovery. 2. Improvements in Descriptor Selection and Model Validation , 2006, J. Chem. Inf. Model..
[11] Sunil Gupta,et al. QSAR analysis of phenolic antioxidants using MOLMAP descriptors of local properties. , 2006, Bioorganic & medicinal chemistry.
[12] Karin Haese,et al. Self-organizing feature maps with self-adjusting learning parameters , 1998, IEEE Trans. Neural Networks.
[13] Peter Ertl,et al. Quest for the rings. In silico exploration of ring universe to identify novel bioactive heteroaromatic scaffolds. , 2006, Journal of medicinal chemistry.
[14] G. Schneider,et al. Predicting Compound Selectivity by Self‐Organizing Maps: Cross‐Activities of Metabotropic Glutamate Receptor Antagonists , 2006, ChemMedChem.
[15] Geoffrey J. Goodhill,et al. Auto-SOM: Recursive Parameter Estimation for Guidance of Self-Organizing Feature Maps , 2001, Neural Computation.
[16] Johann Gasteiger,et al. Prediction of pKa Values for Aliphatic Carboxylic Acids and Alcohols with Empirical Atomic Charge Descriptors , 2006, J. Chem. Inf. Model..
[17] P Schneider,et al. Self-organizing maps in drug discovery: compound library design, scaffold-hopping, repurposing. , 2009, Current medicinal chemistry.
[18] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[19] Tudor I. Oprea,et al. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer‐Aided Drug Repurposing , 2011, Molecular informatics.
[20] Guillaume Bouvier,et al. Automatic clustering of docking poses in virtual screening process using self-organizing map , 2010, Bioinform..
[21] D. Covell,et al. Data mining of NCI's anticancer screening database reveals mitochondrial complex I inhibitors cytotoxic to leukemia cell lines. , 2007, Biochemical pharmacology.
[22] Ling Yang,et al. Classification of Substrates and Inhibitors of P-Glycoprotein Using Unsupervised Machine Learning Approach , 2005, J. Chem. Inf. Model..
[23] Sean Ekins,et al. Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitioning and Kohonen and Sammon mapping techniques. , 2006, Journal of medicinal chemistry.
[24] Paola Gramatica,et al. QSAR study on the tropospheric degradation of organic compounds , 1999 .
[25] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .
[26] G. Schneider,et al. Synergism of virtual screening and medicinal chemistry: identification and optimization of allosteric antagonists of metabotropic glutamate receptor 1. , 2009, Bioorganic & medicinal chemistry.
[27] Denis M. Bayada,et al. Molecular Diversity and Representativity in Chemical Databases , 1999, J. Chem. Inf. Comput. Sci..
[28] Sean Ekins,et al. Shape signatures: new descriptors for predicting cardiotoxicity in silico. , 2008, Chemical research in toxicology.
[29] Tudor I. Oprea,et al. Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps , 2007, J. Chem. Inf. Model..
[30] Peter Ertl,et al. Relationships between Molecular Complexity, Biological Activity, and Structural Diversity , 2006, J. Chem. Inf. Model..
[31] Alexandre Arenas,et al. An Integrated SOM-Fuzzy ARTMAP Neural System for the Evaluation of Toxicity , 2002, J. Chem. Inf. Comput. Sci..
[32] Zhi Wang,et al. Classification of Blood‐Brain Barrier Permeation by Kohonen's Self‐Organizing Neural Network (KohNN) and Support Vector Machine (SVM) , 2009 .
[33] Qing-You Zhang,et al. Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers , 2005, J. Chem. Inf. Model..
[34] J. Gasteiger,et al. Neural networks as data mining tools in drug design , 2003 .
[35] Ersin Bayram,et al. Supervised Self-Organizing Maps in Drug Discovery. 1. Robust Behavior with Overdetermined Data Sets , 2005, J. Chem. Inf. Model..
[36] Johann Gasteiger,et al. A combined application of two different neural network types for the prediction of chemical reactivity , 1993 .
[37] Jorma Laaksonen,et al. SOM_PAK: The Self-Organizing Map Program Package , 1996 .
[38] Andreas Zell,et al. Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors: Dopamine and Benzodiazepine Agonists , 1996, J. Chem. Inf. Comput. Sci..
[39] Tatsuya Takagi,et al. Nonlinear classification of hERG channel inhibitory activity by unsupervised classification method. , 2010, The Journal of toxicological sciences.
[40] Aixia Yan,et al. Classification of Aurora‐A Kinase Inhibitors Using Self‐Organizing Map (SOM) and Support Vector Machine (SVM) , 2011, Molecular informatics.
[41] Alfred Ultsch,et al. Data Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series , 1999 .
[42] Johann Gasteiger,et al. Self-organizing maps for identification of new inhibitors of P-glycoprotein. , 2007, Journal of medicinal chemistry.
[43] Petra Schneider,et al. Self-organizing molecular fingerprints: a ligand-based view on drug-like chemical space and off-target prediction. , 2009, Future medicinal chemistry.
[44] Samuel Kaski,et al. Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..
[45] Gerhard F. Ecker,et al. Classification Models for hERG Inhibitors by Counter‐Propagation Neural Networks , 2008, Chemical biology & drug design.
[46] Johann Gasteiger,et al. Neural networks in chemistry and drug design , 1999 .
[47] J. Gasteiger,et al. The beauty of molecular surfaces as revealed by self-organizing neural networks. , 1994, Journal of molecular graphics.
[48] Emilio Benfenati,et al. Classification of Potential Endocrine Disrupters on the Basis of Molecular Structure Using a Nonlinear Modeling Method , 2004, J. Chem. Inf. Model..
[49] H. Macfie,et al. An application of unsupervised neural network methodology Kohonen topology-Preserving mapping) to QSAR analysis , 1991 .
[50] Sean Ekins,et al. Comprehensive computational assessment of ADME properties using mapping techniques. , 2005, Current drug discovery technologies.
[51] Thomas Sander,et al. Toxicity-Indicating Structural Patterns , 2006, J. Chem. Inf. Model..
[52] Gisbert Schneider,et al. Processing and classification of chemical data inspired by insect olfaction , 2007, Proceedings of the National Academy of Sciences.
[53] Jean-Louis Reymond,et al. Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discovery , 2007, J. Chem. Inf. Model..
[54] Gisbert Schneider,et al. A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries , 2002, Chembiochem : a European journal of chemical biology.