Self‐Organizing Maps for In Silico Screening and Data Visualization

Self‐organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self‐organizing maps: the use of self‐organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.

[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.