Molecular Chemometrics

REFERENCES

• Bohacek , R. S. , McMartin , C. and Guida , W. C. 1996 . The art and practice of structure based drug design: A molecular modeling perspective . Medicinal Research Reviews , 16 : 3 – 50 . [INFOTRIEVE] [CSA]
   PubMed Web of Science ®Google Scholar
• Kubinyi , H. and Müller , G. 2004 . Chemogenomics in Drug Discovery , volume 22 , Weinheim : Wiley-VCH . Methods and Principles in Medicinal Chemistry
   Google Scholar
• Todeschini , R. and Consonni , V. 2000 . Handbook of Molecular Descriptors , volume 11 , New York : Wiley-VCH . Methods and Principles in Medicinal Chemistry
   Google Scholar
• Gasteiger , J. , ed. 2003 . Handbook of Chemoinformatics , Weinheim : Wiley-VCS .
   Google Scholar
• Brock , W. 1992 . The Fontana History of Chemistry , London : Fontana Press .
   Google Scholar
• Dietz , A. 1995 . Yet another representation of molecular structure . Journal of Chemical Information and Computer Sciences , 35 : 787 – 802 . [CROSSREF] [CSA]
   Google Scholar
• Bauerschmidt , S. and Gasteiger , J. 1997 . Overcoming the limitations of a connection table description: A universal representation of chemical species . Journal of Chemical Information and Computer Sciences , 37 : 705 – 714 . [CROSSREF] [CSA]
   Google Scholar
• Baumann , K. 1999 . Uniform-length molecular descriptors for quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR): classication studies and similarity searching . Trends in Analytical Chemistry , 18 : 36 – 46 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Livingstone , D. 2000 . The characterization of chemical structures using molecular properties. A survey . Journal of Chemical Information and Computer Sciences , 40 : 195 – 209 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Duca , J. and Hopfinger , A. 2001 . Estimation of molecular similarity based on 4D-QSAR analysis: Formalism and validation . Journal of Chemical Information and Modeling , 41 : 1367 – 1387 . [CSA]
   Web of Science ®Google Scholar
• Cramer , R. III , Patterson , D. and Bunce , J. 1988 . Comparitative Molecular Field Analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins . Journal of the American Chemical Society , 110 : 5959 – 5967 . [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Kim , K. 1998 . List of CoMFA references, 1997 . Perspectives in Drug Discovery and Design , 12–14 : 334 – 338 . [CROSSREF] [CSA]
   Google Scholar
• Kim , K. , Greco , G. and Novellino , E. 1998 . A critical review of recent CoMFA applications . Perspectives in Drug Discovery and Design , 12–14 : 257 – 315 . [CROSSREF] [CSA]
   Google Scholar
• Hopfinger , A. , Wang , S. , Tokarski , J. , Jin , B. , Albuquerque , M. , Madhav , P. and Duraiswami , C. 1997 . Construction of 3D-QSAR Models using the 4D-QSAR analysis formalism . Journal of the American Chemical Society , 119 : 10509 – 10524 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Senese , C. L. , Duca , J. , Pan , D. , Hopfinger , A. J. and Tseng , Y. J. 2004 . 4D-fingerprints, universal QSAR and QSPR descriptors . Journal of Chemical Information and Computer Sciences , 44 : 1526 – 1539 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Bursi , R. , Dao , Y. , Van Wijk , T. , De Gooyer , M. , Kellenbach , E. and Verwer , P. 1999 . Comparative spectra analysis (CoSA): Spectra as three-dimensional molecular descriptors for the prediction of biological activities . Journal of Chemical Information and Computer Sciences , 39 : 861 – 867 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Begigni , R. , Passerini , L. , Livingstone , D. , Johnson , M. and Giuliani , A. 1999 . Infrared spectra information and their correlation with QSAR descriptors . Journal of Chemical Information and Computer Sciences , 39 : 558 – 562 . [CROSSREF] [CSA]
   Google Scholar
• Beger , R. , Freeman , J. , Lay , J. Jr. , Wilkes , J. and Miller , D. 2001 . Use of 13 C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity . Journal of Chemical Information and Computer Sciences , 41 : 219 – 224 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Asikainen , A. , Ruuskanen , J. and Tuppurainen , K. 2003 . Spectroscopic QSAR methods and self-organizing molecular field analysis for relating molecular structure and estrogenic activity . Journal of Chemical Information and Computer Sciences , 43 : 1974 – 1981 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Bailey , N. , Wang , Y. , Sampson , J. , Davis , W. , Whitcombe , I. , Hylands , P. , Croft , S. and Holmes , E. 2001 . Prediction of anti-plasmodial activity of Artemisia annua extracts: Application of 1H NMR spectroscopy and chemometrics . Journal of Pharmaceutical and Biomedical Analysis , 41 : 219 – 224 . [CSA]
   Google Scholar
• Vanderhoeven , S. , Troke , J. , Tranter , G. , Wilson , I. , Nicholson , J. and Lindon , J. 2004 . Nuclear magnetic resonance (NMR) and quantitative structure-activity relationship (QSAR) studies on the transaceylation reactivity of model 1-O-acyl glucuronides. II: QSAR modelling of the reaction using both computational and experimental NMR parameters . Xenobiotica , 34 : 889 – 900 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Khadikar , P. , Sharma , V. and Varma , R. 2005 . Novel estimation of lipophilicity using 13C NMR chemical shifts as molecular descriptor . Bioorganic and Medicinal Chemistry Letters , 15 : 421 – 425 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Schnackenberg , L. K. and Beger , R. D. 2005 . Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra . Journal of Chemical Information and Modeling , 45 : 360 – 365 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Willighagen , E. , Denissen , H. , Wehrens , R. and Buydens , L. 2006 . On the use of 1H and 13C NMR spectra as QSPR descriptors . Journal of Chemical Information and Computer Sciences , 46 : 487 – 494 . [CSA]
   Google Scholar
• Faulon , J.-L. , Visco , D. P. and Pophale , R. S. 2003 . The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies . Journal of Chemical Information and Computer Sciences , 43 : 707 – 720 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Bremser , W. 1978 . HOSE—A novel substructure code . Analytica Chimica Acta , 103 : 355 – 365 . [CROSSREF] [CSA]
   Google Scholar
• Randi , M. and Basak , S. C. 2001 . A new descriptor for structure-property and structure-activity correlations . Journal of Chemical Information and Computer Sciences , 41 : 650 – 656 . [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Mansfield , M. L. , Covell , D. G. and Jernigan , R. L. 2002 . A new class of molecular shape descriptors. 1. Theory and properties . Journal of Chemical Information and Computer Sciences , 42 : 259 – 273 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Tuppurainen , K. and Ruuskanen , J. 2000 . Electronic eigenvalue (EEVA): a new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. A QSAR approach to the Ah receptor binding affinity of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) . Chemosphere , 41 : 843 – 848 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Stiefl , N. and Baumann , K. 2003 . Mapping property distributions of molecular surfaces: Algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique . Journal of Medicinal Chemistry , 46 : 1390 – 1407 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Lapinsh , M. , Prusis , P. , Gutcaits , A. , Lundstedt , T. and Wikberg , J. E. 2001 . Development of proteo-chemometrics: A novel technology for the analysis of drug-receptor interactions . Biochimica Biophysica Acta , 1525 : 180 – 190 . [CSA]
   PubMed Web of Science ®Google Scholar
• Prusis , P. , Muceniece , R. , Andersson , P. , Post , C. , Lundstedt , T. and Wikberg , J. E. 2001 . PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand-receptor interactions . Biochimica Biophysica Acta , 1544 : 350 – 357 . [CSA]
   PubMed Web of Science ®Google Scholar
• Habershon , S. , Cheung , E. , Harris , K. and Johnston , R. 2004 . Powder Diffraction Indexing as a pattern recognition problem: a new approach for unit cell determinination based on an artificial neural network . Journal of Physical Chemistry A , 108 : 711 – 716 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Willighagen , E. L. , Wehrens , R. , Verwer , P. , de Gelder , R. and Buydens , L. M. C. 2005 . Method for the computational comparison of crystal structures . Acta Crystallographica , B61 : 29 – 36 . [CSA]
   Web of Science ®Google Scholar
• Wehrens , R. , Melssen , W. , Buydens , L. and De Gelder , R. 2005 . Representing structural databases in a self-organizing map . Acta Crystallographica , B61 : 548 – 557 . [CSA]
   Web of Science ®Google Scholar
• Chen , L. and Gasteiger , J. 1996 . Organic reactions classified by neural networks: Michael Additions, Friedel-Crafts alkylations by alkenes, and related reactions . Angewandte Chemie International Edition in English , 35 : 763 – 765 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Chen , L. and Gasteiger , J. 1997 . Knowledge discovery in reaction databases: Landscaping organic reactions by a self-organizing neural network . Journal of the American Chemical Society , 119 : 4033 – 4042 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Zhang , Q.-Y. and de Sousa , J. A. 2005 . Structure-based classification of chemical reactions without assignment of reaction centers . Journal of Chemical Information and Modeling , 45 : 1775 – 1783 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Latino , D. and Aires-de Sousa , J. 2006 . Genome-scale classification of metabolic reactions: A chemoinformatics approach . Angewandte Chemie International Edition in English , 45 : 2066 – 2069 . [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Tipton , K. and Boyce , S. 2000 . History of the enzyme nomenclature system . Bioinformatics , 16 : 34 – 40 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Dobson , C. M. 2004 . Chemical space and biology . Nature , 432 : 824 – 828 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Bender , A. and Glen , R. C. 2004 . Molecular similarity: A key technique in molecular informatics . Organic and Biomolecular Chemistry , 2 : 3204 – 3218 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Nikolova , N. and Jaworska , J. 2003 . Approaches to measure chemical similarity—A review . QSAR & Combinatorial Science , 22 : 1006 – 1026 . [INFOTRIEVE] [CROSSREF] [CSA]
   Google Scholar
• Maldonado , A. G. , Doucet , J. P. , Petitjean , M. and Fan , B.-T. 2006 . Molecular similarity and diversity in chemoinformatics: From theory to applications . Molecular Diversity , 10 : 39 – 79 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• De Gelder , R. , Wehrens , R. and Hageman , J. 2001 . A generalized expression for the similarity spectra: Application to powder diffraction pattern classification . Journal of Computational Chemistry , 22 : 273 – 289 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Gramatica , P. , Pilutti , P. and Papa , E. 2004 . Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling . Journal of Chemical Information and Computer Sciences , 44 : 1794 – 1802 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Olsson , I.-M. , Gottfries , J. and Wold , S. 2004 . Controlling coverage of D-optimal onion designs and selections . Journal of Chemometrics , 18 : 548 – 557 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Wiener , H. 1947 . Structural determination of paraffin boiling points . Journal of the American Chemical Society , 69 : 17 – 20 . [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Hansch , C. and Fujita , T. 1964 . Analysis—A method for the correlation of biological activity and chemical structure . Journal of the American Chemical Society , 86 : 1616 – 1626 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Cortes , C. and Vapnik , V. 1995 . Support-vector networks . Machine Learning , 20 : 273 – 297 . [CSA]
   Web of Science ®Google Scholar
• Walczak , B. and Massart , D. L. 1996 . The radial basis functions—Partial least squares approach as a flexible non-linear regression technique . Analytica Chimica Acta , 331 : 177 – 185 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Ivanciuc , O. 2002 . Structure-odor relationships for pyrazines with support vector machines . Internet Electronic Journal of Molecular Design , 1 : 269 – 284 . [CSA]
   Google Scholar
• Üstün , B. , Melssen , W. and Buydens , L. 2006 . Facilitating the application of support vector regression by using a universal Pearson VII function based kernel . Chemometrics and Intelligent Laboratory Systems , 81 : 29 – 40 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Lind , T. P. 2003 . Support vector machines for the estimation of aqueous solubility . Journal of Chemical Information and Computer Sciences , 43 : 1855 – 1859 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Serra , P. J. R. and Thompson , E. D. 2003 . Development of binary classification of structural chromosome aberrations for a diverse set of organic compounds from molecular structure . Chemical Research in Toxicology , 16 : 153 – 163 . [INFOTRIEVE] [CSA]
   PubMed Web of Science ®Google Scholar
• Byvatov , E. , Fechner , U. , Sadowski , J. and Schneider , G. 2003 . Comparison of support vector machine and artificial neural network systems for drug/nondrug classification . Journal of Chemical Information and Computer Sciences , 43 : 1882 – 1889 . [INFOTRIEVE] [CSA]
   PubMedGoogle Scholar
• Vapnik , V. N. 1995 . The Nature of Statistical Learning Theory , Berlin : Springer .
   Google Scholar
• Burbidge , R. , Trotter , M. , Buxton , B. and Holden , S. 2001 . Drug design by machine learning: Support vector machines for pharmaceutical data analysis . Computers and Chemistry , 26 : 5 – 14 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Song , M. , Breneman , C. , Bi , J. , Sukumar , N. , Bennett , B. , Cramer , S. and Tugcu , N. 2002 . Prediction of protein retention times in anion-exchange chromatography systems using support vector regression . Journal of Chemical Information and Computer Sciences , 42 : 1347 – 1357 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Kazius , J. , McGuire , R. and Bursi , R. 2005 . Derivation and validation of toxicophores for mutagenicity prediction . Journal of Medicinal Chemistry , 48 : 312 – 320 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Borgelt , C. , Meinl , T. and Berthold , M. 2005 . MoSS: A program for molecular substructure mining , Proceedings of OSDM 2005 Edited by: Goethals , B. , Nijssen , S. and Zaki , M. J. 6 – 15 .
   Google Scholar
• Helma , C. , Cramer , T. , Kramer , S. and Raedt , L. D. 2004 . Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds . Journal of Chemical Information and Computer Sciences , 44 : 1402 – 1411 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Leardi , R. 2001 . Genetic algorithms in chemometrics and chemistry: A review . Journal of Chemometrics , 15 : 559 – 569 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Xu , L. and Zhang , W.-J. 2001 . Comparison of different methods for variable selection . Analytica Chimica Acta , 446 : 477 – 483 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Hageman , J. , Streppel , M. , Wehrens , R. and Buydens , L. 2003 . Wavelength Selection with Tabu Search . Journal of Chemometrics , 17 : 427 – 437 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Jurs , P. and Sutter , J. 1995 . “ Adaption of simulated annealing to chemical optimization problems ” . In Data Handling in Science and Technology , volume 15 , Amsterdam : Elsevier . chapter Selection of molecular descriptors for quantitative structure-activity relationships
   Google Scholar
• Shen , Q. , Jiang , J.-H. , Tao , J.-C. , Shen , G.-L. and Yu , R.-Q. 2005 . Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors . Journal of Chemical Information and Modeling , 45 : 1024 – 1029 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Shen , Q. , Jiang , J.-H. , Jiao , C.-X. , Shen , G.-L. and Yu , R.-Q. 2004 . Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists . European Journal of Pharmaceutical Sciences , 22 : 145 – 152 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Byvatov , E. and Schneider , G. 2004 . SVM-based feature selection for characterization of focused compound collections . Journal of Chemical Information and Computer Sciences , 44 : 993 – 999 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Hawkins , D. M. 2004 . The problem of overfitting . Journal of Chemical Information and Computer Sciences , 44 : 1 – 12 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Baumann , K. , Albert , H. and Von Korff , M. 2002 . A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Practical applications . Journal of Chemometrics , 16 : 351 – 360 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Hawkins , D. , Basak , S. and Mills , D. 2003 . Accessing model fit by cross-validation . Journal of Chemical Information and Computer Sciences , 43 : 579 – 586 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Golbraikh , A. and Tropsha , A. 2002 . Beware of q2 . Journal of Molecular Graphics and Modelling , 20 : 269 – 276 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Wold , S. and Eriksson , L. 1995 . “ Statistical validation of QSAR results ” . In Chemometrics Methods in Molecular Design , 309 – 318 . Weinheim, , Germany : VCH .
   Google Scholar
• Wehrens , R. and Van der Linden , W. 1997 . Bootstrapping principal-component regression models . Journal of Chemometrics , 11 : 157 – 171 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Mevik , B.-H. and Cederkvist , H. 2004 . Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR) . Journal of Chemometrics , 18 : 422 – 429 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Todeschini , R. , Consonni , V. , Mauri , A. and Pavan , M. 2003 . Detecting “bad” regression models: Multicriteria fitness functions in regression analysis . Analytica Chimica Acta , 515 : 199 – 208 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Eriksson , L. , Jaworska , J. , Worth , A. P. , Cronin , M. T. D. , McDowell , R. M. and Gramatica , P. 2003 . Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs . Environmental Health Perspectives , 111 : 1361 – 1375 . [INFOTRIEVE] [CSA]
   PubMed Web of Science ®Google Scholar
• Tropsha , A. , Gramatica , P. and Gombar , V. K. 2003 . The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models . QSAR & Combinatorial Science , 22 : 69 – 77 . [INFOTRIEVE] [CROSSREF] [CSA]
   Google Scholar
• Lavine , B. 1998 . Chemometrics . Analytical Chemistry , 70 : 209 – 228 . [CROSSREF] [CSA]
   Web of Science ®Google Scholar
• Lavine , B. 2000 . Chemometrics . Analytical Chemistry , 72 : 91 – 98 . [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Lavine , B. and Workman , J. 2002 . Chemometrtics . Analytical Chemistry , 74 : 2763 – 2770 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Lavine , B. and Workman , J. 2004 . Chemometrics . Analytical Chemistry , 76 : 3365 – 3372 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Berners-Lee , T. , Hendler , J. and Lassila , O. 2001 . The Semantic Web . Scientific American , : 28 – 37 . [CSA]
   Google Scholar
• Coles , S. J. , Day , N. E. , Murray-Rust , P. , Rzepa , H. S. and Zhang , Y. 2005 . Enhancement of the chemical semantic web through the use of InChI identifiers . Organic and Biomolecular Chemistry , 3 : 1832 – 1834 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Murray-Rust , P. , Rzepa , H. S. , Tyrrell , S. M. and Zhang , Y. 2004 . Representation and use of chemistry in the global electronic age . Organic & Biomolecular Chemistry , 2 : 3192 – 3203 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Guha , R. , Howard , M. , Hutchison , G. , Murray-Rust , P. , Rzepa , R. , Steinbeck , S. , Wegner , J. and Willighagen , E. 2006 . The blue obelisk-interoperability in chemical informatics . Journal of Chemical Information and Modeling , 46 : 991 – 998 . [INFOTRIEVE] [CSA]
   PubMed Web of Science ®Google Scholar
• Chen , J. , Swamidass , S. J. , Dou , Y. , Bruand , J. and Baldi , P. 2005 . ChemDB: A public database of small molecules and related chemoinformatics resources . Bioinformatics , 21 : 4133 – 4139 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Kanehisa , M. , Goto , S. , Kawashima , S. and Nakaya , A. 2002 . The KEGG databases at GenomeNet . Nucleic Acids Research , 30 : 42 – 46 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• von Grotthuss , M. , Koczyk , G. , Pas , J. , Wyrwicz , L. S. and Rychlewski , L. 2004 . Ligand. Info small-molecule Meta-Database . Combinatorial Chemistry and High Throughput Screening , 7 : 757 – 761 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Holliday , G. L. , Bartlett , G. J. , Almonacid , D. E. , O'Boyle , N. M. , Murray-Rust , P. , Thornton , J. M. and Mitchell , J. B. O. 2005 . MACiE: a database of enzyme reaction mechanisms . Bioinformatics , 21 : 4315 – 4316 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Steinbeck , C. , Kuhn , S. and Krause , S. 2003 . NMRShiftDB—Constructing a chemical information system with open source components . Journal of Chemical Information and Computer Sciences , 43 : 1733 – 1739 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Austin , C. P. , Brady , L. S. , Insel , T. R. and Collins , F. S. 2004 . NIH Molecular Libraries Initiative . Science , 306 : 1138 – 1139 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Berman , H. , Henrick , K. and Nakamura , H. 2003 . Announcing the worldwide Protein Data Bank . Nature Structural Biology , 10 : 980 [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Irwin , J. and Shoichet , B. 2005 . ZINC—A free database of commercially available compounds for virtual screening . Journal of Chemical Information and Modeling , 45 : 177 – 182 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Murray-Rust , P. and Rzepa , H. 1999 . Chemical Markup XML and the Worldwide Web. 1. Basic principles . Journal of Chemical Information and Computer Sciences , 39 : 928 – 942 . [CROSSREF] [CSA]
   Google Scholar
• Holliday , G. L. , Murray-Rust , P. and Rzepa , H. S. 2006 . Chemical Markup, XML, and the World Wide Web. 6. CMLReact, an XML Vocabulary for chemical reactions . Journal of Chemical Information and Modeling , 46 : 145 – 157 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Murray-Rust , P. , Rzepa , H. S. , Williamson , M. J. and Willighagen , E. L. 2004 . Chemical markup, XML, and the World Wide Web. 5. Applications of chemical metadata in RSS aggregators . Journal of Chemical Information and Computer Sciences , 44 : 462 – 469 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar
• Banville , D. L. 2006 . Mining chemical structural information from the drug literature . Drug Discovery Today , 11 : 35 – 42 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Townsend , J. A. , Adams , S. E. , Waudby , C. A. , de Souza , V. K. , Goodman , J. M. and Murray-Rust , P. 2004 . Chemical documents: Machine understanding and automated information extraction . Organic & Biomolecular Chemistry , 2 : 3294 – 3300 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Karthikeyan , M. , Krishnan , S. and Pandey , A. 2006 . Harvesting chemical information from the internet using a distributed approach: ChemXtreme . Journal of Chemical Information and Modeling , 46 : 452 – 461 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMed Web of Science ®Google Scholar
• Stein , S. , Heller , S. and Tchekhovski , D. An open standard for chemical structure representation—The IUPAC Chemical Identier . Nimes International Chemical Information Conference Proceedings . pp. 131 – 143 .
   Google Scholar
• Singh , S. B. , Hull , R. D. and Fluder , E. M. 2003 . Text Influenced Molecular Indexing TIMI): A literature database mining approach that handles text and chemistry . Journal of Chemical Information and Computer Sciences , 43 : 743 – 752 . [INFOTRIEVE] [CROSSREF] [CSA]
   PubMedGoogle Scholar