Source: http://www.google.com/patents/US7039621?dq=Xerox+%2B+%22centroid
Timestamp: 2014-07-23 02:03:17
Document Index: 518041992

Matched Legal Cases: ['art 1', 'art 1', 'art 1', 'art 1', 'art 1', 'art 1', 'art 1', 'art 1']

Patent US7039621 - System, method, and computer program product for representing object ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA method and computer product is presented for mapping n-dimensional input patterns into an m-dimensional space so as to preserve relationships that may exist in the n-dimensional space. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping...http://www.google.com/patents/US7039621?utm_source=gb-gplus-sharePatent US7039621 - System, method, and computer program product for representing object relationships in a multidimensional spaceAdvanced Patent SearchPublication numberUS7039621 B2Publication typeGrantApplication numberUS 09/814,160Publication dateMay 2, 2006Filing dateMar 22, 2001Priority dateMar 22, 2000Fee statusPaidAlso published asUS20020091655, WO2001071624A1, WO2001071624A9Publication number09814160, 814160, US 7039621 B2, US 7039621B2, US-B2-7039621, US7039621 B2, US7039621B2InventorsDimitris K. Agrafiotis, Dmitri N. Rassokhin, Victor S. Lobanov, F. Raymond SalemmeOriginal AssigneeJohnson & Johnson Pharmaceutical Research & Development, L.L.C.Export CitationBiBTeX, EndNote, RefManPatent Citations (82), Non-Patent Citations (99), Referenced by (14), Classifications (14), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetSystem, method, and computer program product for representing object relationships in a multidimensional spaceUS 7039621 B2Abstract A method and computer product is presented for mapping n-dimensional input patterns into an m-dimensional space so as to preserve relationships that may exist in the n-dimensional space. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping process. A set of locally defined neural networks is created, then trained in accordance with the mapping produced by the iterative process. Additional input patterns not in the subset are mapped into the m-dimensional space by using one of the local neural networks. In an alternative embodiment, the local neural networks are only used after training and use of a global neural network. The global neural network is trained in accordance with the mapping produced by the iterative process. Input patterns are initially projected into the m-dimensional space using the global neural network. Local neural networks are then used to refine the results of the global network.
E = ∑ i < j k ⁢ [ r ij - d ij ] 2 r ij ∑ i < j k ⁢ r ij ( 2 ) which is minimized using steepest descent. The initial coordinates, yi, are determined at random or by some other projection technique such as principal componenet analysis, and are updated using Eq. 3:
Δ ij ⁡ ( t ) = ∂ E ⁡ ( t ) ∂ y ij ⁡ ( t )  ∂ 2 ⁢ E ⁡ ( t ) ∂ y ij ⁡ ( t ) 2  ( 4 ) There is a wide variety of MDS algorithms involving different error functions and optimization heuristics, which are reviewed in Schiffman, Reynolds and Young, Introduction to Multidimensional Scaling, Academic Press, New York (1981); Young and Hamer, Multidimensional Scaling: History, Theory and Applications, Erlbaum Associates, Inc., Hillsdale, N.J. (1987); Cox and Cox, Multidimensional Scaling, Number 59 in Monographs in Statistics and Applied Probability, Chapman-Hall (1994), and Borg, I., Groenen, P., Modem Multidimensional Scaling, Springer-Verlag, New York, (1997). The contents of these publications are incorporated herein by reference in their entireties. Different forms of NLM will be discussed in greater detail below.
y i ⁡ ( t + 1 ) = 0.5 ⁢ ⁢ λ ⁡ ( t ) ⁢ r ij - d ij ⁡ ( t ) d ij ⁡ ( t ) ⁢ ( y i ⁡ ( t ) - y j ⁡ ( t ) ) ( 6 ) where t is the iteration number, dij=∥yi(t)−yj(t)∥, and λ(t) is an adjustable parameter, referred to hereafter as the �learning rate�. This process is repeated for a fixed number of cycles, or until some global error criterion is minimized within some prescribed tolerance. A large number of iterations are typically required to achieve statistical accuracy.
λ ⁡ ( t ) = ( λ min + t ⁢ λ max - λ min T ) ⁢ ⁢ 1 1 + ar ij ⁢ ⁢ or Eq. 8: ( 7 ) λ ⁡ ( t ) = ( λ min + t ⁢ λ max - λ min T ) ⁢ ⅇ - ar ij ( 8 ) where λmax and λmin are the (unweighted) starting and ending learning rates such that λmax, λmin, ∈[0,1], T is the total number of refinement steps (iterations), t is the current iteration number, and α is a constant scaling factor. EQ. 7 and 8 have the effect of decreasing the correction at large separations (weak relationships), thus creating a nonlinear map which preserves strong relationships (short distances) more faithfully than weak ones. Weighting is discussed in greater detail below.
J q = ∑ j = 1 C ⁢ ∑ i = 1 N ⁢ m ij q ⁢ d 2 ⁡ ( x i , c j ) ( 12 ) over a set of points xi, i=1,2, . . . , N, with respect to the fuzzy degrees of membership mij, the �fuzziness� index q, and the cluster centroids cj, j=1, 2, . . . C, where C is the total number of clusters, and mij is the degree of membership of the i-th pattern to the j-th cluster. In addition, the solution must satisfy the constraints that the membership value of each pattern in any given cluster must be less than or equal to one:
∑ j = 1 C ⁢ m ij = 1 ( 14 ) In an embodiment of the invention, the squared distance d2(xi, cj) is the Euclidean distance between the i-th point in Rn and the j-th reference point. Equations 13 and 14 ensure that the solutions to Eq. 6 represent true fuzzy partitions of the data set among all the specified classes. In the above equation, q∈[1∞) is a weighting exponent known as the �fuzziness index� or �fuzzifier� that controls the degree of fuzziness of the resulting clusters. For q=1 the partitions are crisp, and as q→∞ the clusters become increasingly fuzzy.
m ij = [ 1 d 2 ⁡ ( x i , c j ) ] 1 q - 1 ∑ k = 1 C ⁢ [ 1 d 2 ⁡ ( x i , c k ) ] 1 q - 1 ( 15 ) In step 425, the objective function Jq p is evaluated using Eq. 16:
J q p = ∑ j = 1 C ⁢ ∑ i = 1 N ⁢ m ij q ⁢ d 2 ⁡ ( x i , c j ) ( 16 ) In step 430, new centroids cj are computed using the formula:
c j = ∑ i = 1 N ⁢ m ij q ⁢ x i ∑ i = 1 N ⁢ m ij q ( 17 ) In step 435, mij are recalculated using the new centroids and Eq. 15. In step 440, the iteration counter p is incremented. In step 445, Jq p is recalculated in light of the new mij determined in step 435.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS4773099Oct 10, 1985Sep 20, 1988The Palantir CorporationPattern classification means for use in a pattern recognition systemUS4811217Mar 25, 1986Mar 7, 1989Japan Association For International Chemical InformationMethod of storing and searching chemical structure dataUS4859736Mar 21, 1988Aug 22, 1989Ciba-Geigy CorporationSynthetic polystyrene resin and its use in solid phase peptide synthesisUS4908773Apr 6, 1987Mar 13, 1990Genex CorporationComputer designed stabilized proteins and method for producing sameUS4935875Feb 13, 1989Jun 19, 1990Data Chem, Inc.Chemical analyzerUS4939666Sep 2, 1987Jul 3, 1990Genex CorporationIncremental macromolecule construction methodsUS5010175May 2, 1988Apr 23, 1991The Regents Of The University Of CaliforniaReacting activated amino acids with an acceptor amino acid or peptide in relative concentrations based on relative coupling constants so that the resulting mixture of peptides contains each peptide in predictable amountsUS5025388Aug 26, 1988Jun 18, 1991Cramer Richard D IiiComparative molecular field analysis (CoMFA)US5095443Oct 4, 1989Mar 10, 1992Ricoh Company, Ltd.Plural neural network system having a successive approximation learning methodUS5155801Oct 9, 1990Oct 13, 1992Hughes Aircraft CompanyClustered neural networksUS5167009Aug 3, 1990Nov 24, 1992E. I. Du Pont De Nemours & Co. (Inc.)On-line process control neural network using data pointersUS5181259Sep 25, 1990Jan 19, 1993The United States Of America As Represented By The Administrator Of The National Aeronautics And Space AdministrationGeneral method of pattern classification using the two domain theoryUS5240680Dec 19, 1991Aug 31, 1993Chiron CorporationAutomated apparatus for use in peptide synthesisUS5260882Jan 2, 1991Nov 9, 1993Rohm And Haas CompanyProcess for the estimation of physical and chemical properties of a proposed polymeric or copolymeric substance or materialUS5265030Aug 19, 1992Nov 23, 1993Scripps Clinic And Research FoundationSystem and method for determining three-dimensional structures of proteinsUS5270170Oct 16, 1991Dec 14, 1993Affymax Technologies N.V.Peptide library and screening methodUS5276771 *Dec 27, 1991Jan 4, 1994R & D AssociatesData processing systemUS5288514Sep 14, 1992Feb 22, 1994The Regents Of The University Of CaliforniaLinking a protected 2-aminobenzophenone to a substrate; deprotecting and reacting with an amino acid; cyclization; removal from substrate; screeningUS5307287Jun 17, 1991Apr 26, 1994Tripos Associates, Inc.Comparative molecular field analysis (COMFA)US5323471Mar 30, 1992Jun 21, 1994Atr Auditory And Visual Perception Research LaboratoriesPattern recognition apparatus and pattern learning apparatus employing neural net including excitatory element-inhibitory element pair couplingsUS5331573Dec 14, 1990Jul 19, 1994Balaji Vitukudi NMethod of design of compounds that mimic conformational features of selected peptidesUS5434796Jun 30, 1993Jul 18, 1995Daylight Chemical Information Systems, Inc.Method and apparatus for designing molecules with desired properties by evolving successive populationsUS5436850Mar 28, 1994Jul 25, 1995The Regents Of The University Of CaliforniaMethod to identify protein sequences that fold into a known three-dimensional structureUS5442122Nov 3, 1993Aug 15, 1995Shimadzu CorporationDibenzosuberyl and dibenzosuberenyl derivativesUS5461697 *Nov 12, 1993Oct 24, 1995Sekisui Kagaku Kogyo Kabushiki KaishaSpeaker recognition system using neural networkUS5463564Sep 16, 1994Oct 31, 19953-Dimensional Pharmaceuticals, Inc.System and method of automatically generating chemical compounds with desired propertiesUS5499193Jun 21, 1993Mar 12, 1996Takeda Chemical Industries, Ltd.Automated synthesis apparatus and method of controlling the apparatusUS5519635Sep 6, 1994May 21, 1996Hitachi Ltd.Apparatus for chemical analysis with detachable analytical unitsUS5524065Jul 25, 1994Jun 4, 1996Canon Kabushiki KaishaMethod and apparatus for pattern recognitionUS5526281Oct 28, 1994Jun 11, 1996Arris Pharmaceutical CorporationMachine-learning approach to modeling biological activity for molecular design and to modeling other characteristicsUS5545568Dec 2, 1993Aug 13, 1996The Regents Of The University Of CaliforniaSolid phase and combinatorial synthesis of compounds on a solid supportUS5549974Jun 23, 1994Aug 27, 1996Affymax Technologies NvImmobilizationUS5553225Oct 25, 1994Sep 3, 1996International Business Machines CorporationMethod and apparatus for combining a zoom function in scroll bar slidersUS5565325Jun 1, 1995Oct 15, 1996Bristol-Myers Squibb CompanyIterative methods for screening peptide librariesUS5574656Sep 28, 1995Nov 12, 19963-Dimensional Pharmaceuticals, Inc.System and method of automatically generating chemical compounds with desired propertiesUS5585277Mar 21, 1995Dec 17, 1996Scriptgen Pharmaceuticals, Inc.Determining ability of test ligands to increase the ratio of folded target protein to unfolded target protein; testing for possible pharmaceutical effectivenessUS5602755Jun 23, 1995Feb 11, 1997Exxon Research And Engineering CompanyMethod for predicting chemical or physical properties of complex mixturesUS5602938May 20, 1994Feb 11, 1997Nippon Telegraph And Telephone CorporationMethod of generating dictionary for pattern recognition and pattern recognition method using the sameUS5612895Apr 21, 1995Mar 18, 1997Balaji; Vitukudi N.Method of rational drug design based on ab initio computer simulation of conformational features of peptidesUS5621861Jun 21, 1994Apr 15, 1997Matsushita Electric Industrial Co., Ltd.Method of reducing amount of data required to achieve neural network learningUS5634017Jun 21, 1996May 27, 1997International Business Machines CorporationComputer system and method for processing atomic data to calculate and exhibit the properties and structure of matter based on relativistic modelsUS5635598Jun 21, 1994Jun 3, 1997Selectide CorporationSelectively cleavabe linners based on iminodiacetic acid esters for solid phase peptide synthesisUS5670326Jun 7, 1995Sep 23, 1997Pharmagenics, Inc.Reiterative method for screening combinatorial librariesUS5679582Jun 21, 1994Oct 21, 1997Scriptgen Pharmaceuticals, Inc.Screening method for identifying ligands for target proteinsUS5684711Aug 15, 1996Nov 4, 19973-Dimensional Pharmaceuticals, Inc.System, method, and computer program for at least partially automatically generating chemical compounds having desired propertiesUS5703792Dec 13, 1994Dec 30, 1997Arris Pharmaceutical CorporationThree dimensional measurement of molecular diversityUS5712171Jan 20, 1995Jan 27, 1998Arqule, Inc.Method of generating a plurality of chemical compounds in a spatially arranged arrayUS5712564Dec 29, 1995Jan 27, 1998Unisys CorporationMagnetic ink recorder calibration apparatus and methodUS5729662 *Jun 7, 1995Mar 17, 1998Rozmus; J. MichaelNeural network for classification of patterns with improved method and apparatus for ordering vectorsUS5734796 *Sep 29, 1995Mar 31, 1998Ai Ware, Inc.Self-organization of pattern data with dimension reduction through learning of non-linear variance-constrained mappingUS5736412May 17, 1996Apr 7, 1998Arqule, Inc.Method of generating a plurality of chemical compounds in a spatially arranged arrayUS5740326Jun 7, 1995Apr 14, 1998International Business Machines CorporationCircuit for searching/sorting data in neural networksUS5789160Jun 5, 1995Aug 4, 1998Nexstar Pharmaceuticals, Inc.Parallel selexUS5807754May 11, 1995Sep 15, 1998Arqule, Inc.Combinatorial synthesis and high-throughput screening of a Rev-inhibiting arylidenediamide arrayUS5811241Sep 13, 1995Sep 22, 1998Cortech, Inc.Method for preparing and identifying N-substitued 1,4-piperazines and N-substituted 1,4-piperazinedionesUS5832494May 17, 1996Nov 3, 1998Libertech, Inc.Method and apparatus for indexing, searching and displaying dataUS5858660Mar 20, 1996Jan 12, 1999Nexstar Pharmaceuticlas, Inc.Parallel selexUS5861532Mar 4, 1997Jan 19, 1999Chiron CorporationSolid-phase synthesis of N-alkyl amidesUS5866334Jul 16, 1996Feb 2, 1999Genzyme CorporationDetermination and identification of active compounds in a compound libraryUS5901069Aug 1, 1997May 4, 19993-Dimensional Pharmaceuticals, Inc.System, method, and computer program product for at least partially automatically generating chemical compounds with desired properties from a list of potential chemical compounds to synthesizeUS5908960Apr 27, 1998Jun 1, 1999Smithkline Beecham CorporationLibraries of compounds based upon a n-benzyl-n-alkyl-4-alkoxybenzamide, n-benzyl-n-alkyl-4-alkoxyphenylacetamide and n-benzyl-n-alkyl-3-(4-alkoxyphenyl)propionamide template toUS5933819May 23, 1997Aug 3, 1999The Scripps Research InstituteComputer-implemented methodUS6014661May 2, 1997Jan 11, 2000Ivee Development AbSystem and method for automatic analysis of data bases and for user-controlled dynamic queryingUS6037135Nov 29, 1993Mar 14, 2000Epimmune Inc.Methods for making HLA binding peptides and their usesUS6049797Apr 7, 1998Apr 11, 2000Lucent Technologies, Inc.Method, apparatus and programmed medium for clustering databases with categorical attributesUS6134537 *Dec 15, 1997Oct 17, 2000Ai Ware, Inc.Visualization and self organization of multidimensional data through equalized orthogonal mappingUS6185506Jan 26, 1996Feb 6, 2001Tripos, Inc.Method for selecting an optimally diverse library of small molecules based on validated molecular structural descriptorsUS6192351 *Feb 23, 1996Feb 20, 2001Osmetech PlcFuzzy neural networksEP0355266B1Apr 27, 1989Jun 9, 1993Millipore CorporationApparatus for performing repetitive chemical reactionsEP0355628B1Aug 11, 1989Nov 10, 1993Siemens AktiengesellschaftProcess for chemically decontaminating the surface of a metallic construction element of a nuclear power plantEP0770876A1Oct 17, 1996May 2, 1997Scriptgen Pharmaceuticals, Inc.A screening method for identifying ligands for target proteinsEP0818744A2Jun 24, 1997Jan 14, 1998Proteus Molecular Design LimitedProcess for selecting candidate drug compoundsWO1991019735A1Jun 14, 1991Dec 15, 1991Paul A BartlettLibraries of modified peptides with protease resistanceWO1992000091A1Jul 1, 1991Jan 9, 1992Bioligand IncRandom bio-oligomer library, a method of synthesis thereof, and a method of use thereofWO1993020242A1Mar 30, 1993Oct 14, 1993Sydney BrennerEncoded combinatorial chemical librariesWO1994028504A1May 20, 1994Dec 8, 1994Arris PharmaceuticalA machine-learning approach to modeling biological activity for molecular design and to modeling other characteristicsWO1995001606A1Jun 29, 1994Jan 12, 1995Daylight Chem Inf Syst IncMethod and apparatus for designing molecules with desired properties by evolving successive populationsWO1997009342A1Sep 6, 1996Mar 13, 1997Scriptgen Pharm IncScreen for compounds with affinity for rnaWO1997020952A1Dec 6, 1996Jun 12, 1997Scriptgen Pharm IncA fluorescence-based screening method for identifying ligandsWO1997027559A1Jan 27, 1997Jul 31, 1997David E PattersonMethod of creating and searching a molecular virtual library using validated molecular structure descriptorsWO1998020437A2Nov 4, 1997May 14, 1998Dimensional Pharm IncSystem, method and computer program product for identifying chemical compounds having desired propertiesWO1998020459A1Nov 4, 1997May 14, 1998Dimensional Pharm IncSystem, method, and computer program product for the visualization and interactive processing and analysis of chemical data* Cited by examinerNon-Patent CitationsReference1Agrafiotis, D., "Theoretical Aspects of the Complex: Arts and New Technologies," Applications and Impacts Information Processing '94, North-Holland, vol. II, 1994, pp. 714-719.2Agrafiotis, D.K. and Jaeger, E.P., "Directed Diversity(R): An Operating System For Combinatorial Chemistry," Abstracts of Papers Part 1: 211th ACS National Meeting, Mar. 24-28, 1996, p. 46-COMP.3Agrafiotis, D.K. and Jaeger, E.P., "Stochastic Algorithms for Exploring Molecular Diversity." Abstracts of Papers Part 1: 213th ACS National Meeting, APr. 13-17, 1997, p. 16-CINF.4Agrafiotis, D.K. and Lobanov, V.S., "An Efficient Implementation of Distance-Based Diversity Measures Based on k-d Trees," J. Chem. Inf. Comput. Sci., American Chemical Society, vol. 39, No. 1, Jan./Feb. 1999, pp. 51-58.5Agrafiotis, D.K. and Lobanov, V.S., "Bridging The Gap Between Diversity And QSAR," Abstracts of Papers Part 1: 215th ACS National Meeting, Mar. 29-Apr. 2, 1998, p. 181-COMP.6Agrafiotis, D.K. et al., "Advances in diversity profiling and combinatorial series design," Molecular Diversity, Kluwer Academic Publishers, vol. 4, 1999, pp. 1-22.7Agrafiotis, D.K. et al., "Parallel QSAR", Abstracts of Papers Part 1: 217th ACS National Meeting, Mar. 21-25, 1999, p. 50-COMP.8Agrafiotis, D.K. et al., "PRODEN: A New Program for Calculating Integrated Projected Populations," Journal of Computational Chemistry, John Wiley & sons, Inc., vol. 11, No. 9, Oct. 1990, pp. 1101-1110.9Agrafiotis, D.K., "A New Method For Analyzing Protein Sequence Relationships Based On Sammon Maps," Protein Science, Cambridge University Press, vol. 6, No. 2, Feb. 1997, pp. 287-293.10Agrafiotis, D.K., "Diversity of Chemical Libraries," Encyclopedia of Computational Chemistry, John Wiley & Sons Ltd, vol. 1:A-D, 1998, pp. 742-761.11Agrafiotis, D.K., "On the Use of Information Theory for Assessing Molecular Diversity," Journal of Chemistry Information and Computer Sciences, American Chemical Society, vol. 37, No. 3, May/Jun. 1997, pp. 576-580.12Agrafiotis, Dimitris K. and Lobanov, Victor S., "Ultrafast Algorithm for Designing Focused Combinational Arrays," J. Chem. Inf. Comput. Sci., American Chemical Society, 2000, vol. 40, No. 4, pp. 1030-1038.13Ajay et al., "Can We Learn To Distinguish between 'Drug-Like' Molecules?" Journal of Medicinal Chemistry, 1998, American Chemical Society, vol. 41, No. 18, pp. 3314-3324.14Amzel, L.M., "Structure-based drug design," Current Opinion in Biotechnology, vol. 9, No. 4, Aug. 1998, pp. 366-369.15Barnard, John M. and Downs, Geoff M., "Computer representation and manipulation of combinatorial libraries," Perspectives in Drug Discovery and Design, Kluwer Academic Publishers, 1997, pp. 13-30.16Bellman, R.E., Adaptive Control Processes: A Guided Tour, Princeton Univ. Press, Princeton, NJ (1961), entire book submitted.17Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY (1981), entire book submitted.18Biswas, G. et al., "Evaluation of Projection Algorithms," IEEE Transactions On Pattern Analysis And Machine Intelligence, IEEE Computer Society, vol. PAMI-3, No. 6, Nov. 1981, pp. 701-708.19Blaney, J.M. and Martin, E.J., "Computational approaches for combinatorial library design and molecular diversity analysis," Current Opinion Chemical Biology, Current Biology Ltd., vol. 1, No. 1, Jun. 1997, pp. 54-59.20Bonchev, D. and Trinajstic, N., "Information theory, distance matrix, and molecular branching," The Journal of Chemical Physics, American Institute of Physics, vol. 67, No. 10, Nov. 15, 1977, pp. 4517-4533.21Borg, Ingwer and Groenen, Patrick, Modern Multidimensional Scaling Theory and Applications, Springer Series in Statistics, 1997, entire book submitted.22Brint, Andrew T. and Willett, Peter, "Upperbound procedures for the identification of similar three-dimensional chemical structures," Journal of Computer-Aided Molecular Design, ESCOM Science Publishers B.V., vol. 2, No. 4, Jan. 1989, pp. 311-320.23Brown, R.D. and Clark, D.E., "Genetic diveristy: applications of evolutionary algorithms to combinatorial library design," Expert Opinion on Therapeutic Patents, vol. 8, No. 11, Nov. 1998, pp. 1447-1459.24Brown, Robert D. and Martin, Yvonne C., "Designing Combinatorial Library Mixtures Using a Genetic Algorithm," Journal of Medicinal Chemistry, American Chemical Society, vol. 40, No. 15, 1997, pp. 2304-2313.25Brown, Robert D. and Martin, Yvonne C., "the Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1997, vol. 37, No. 1, pp. 1-9.26Brown, Robert D. and Martin, Yvonne C., "Use of Structur-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1996, vol. 36, No. 3, pp. 572-584.27Caflisch, A. and Karplus, M., "Computational combinatorial chemitry for de novo ligand design: Review and assessment," Perspectives in Drug Discovery and Design, ESCOM Science Publishers B.V., vol. 3, 1995, pp. 51-84.28Chang, C.L. and Lee, R.C.T., "A Heuristic Relaxation Method for Nonlinear Mapping in Cluster Analysis," IEEE Transactions on Systems, Man, and Cybernetics, IEEE Systems, MAn, and Cybernetics Society, vol. SMC-3, Mar. 1973, pp. 197-200.29Copy of International Search Report issued Oct. 18, 1999, for Appl. No. PCT/US99/09963, 7 pages.30Cramer, R.D. et al., "Virtual Compound Libraries: A New Approach to Decision Making in Molecular Discovery Research," Journal of Chemical Information and Computer Science, American Chemical Society, vol. 38, No. 6, Nov./Dec. 1998, pp. 1010-1023.31Cummins, David J. et al., "Molecular Diversity in Chemical Databases: Comparison of Medicinal Chemistry Knowledge Bases and Databases of Commerically Available Compounds," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1996, vol. 36, No. 4, pp. 750-763.32Danheiser, S.L., "Current Trends in Synthetic Peptide and Chemical Diversity Library Design," Genetic Engineering News, May 1, 1994, pp. 10 and 31.33Daylight Theory: Fingerprints (visited Sep. 26, 2000) <http://www.daylight.com/dayhtml/doc/theory/theory.finger.html>, 9 pages.34Daylight Theory: SMARTS (visited Sep. 26, 2000) <http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html>, 10 pages.35DeMers, D. and Cottrell, G., "Non-Linear Dimensionality Reduction," Advances in Neural Information Processing Systems, vol. 5, 1993, pp. 580-587.36Downs, Geoff M. and Barnard, John M., "Techniques for Generating Descriptive Fingerprints in Combinatorial Libraries," J. Chem. Inf. Comput. Sci., American Chemical Society, 1997, vol. 37, No. 1, pp. 59-61.37Eichler, U. et al., "Addressing the problem of molecular diversity," Drugs of the Future, Prous Science, vol. 24, No. 2, 1999, pp. 177-190.38English-language Abstract of European Patent No. 0 355 628, printed from Dialog File No. 351(Feb., 1990-Date of publication of application).39Felder, E.R. and Poppinger, D., "Combinatorial Compound Libraries for Enhanced Drug Discovery Approached," Advances in Drug Research, Academic Press, vol. 30, 1997, pp. 112-199.40Frey, P.W. and Slate, D.J., "Letter Recognition Using Holland-Style Adaptive Classifiers," Machine Learning, Kluwer Academic Publishers, vol. 6, 1991, pp. 161-182.41Friedman J.H. and Turkey, J.W., "A Projection Pursuit Algorithm for Exploratory Data Analysis," IEEE Transactions on Computers, IEEE Computer Society, vol. C-23, No. 9, Sep. 1974, pp. 881-889.42Friedman, J.H., "Exploratory Projection Pursuit," Journal of the American Statistical Association, American Statistical Association, vol. 82, No. 397, Mar. 1987, pp. 249-266.43Garrido, L. et al., "Use of Multilayer Feedforward Neural Nets As A Display Method for Multidimensional Distributions," International Journal of Neural Systems, World Scientific Publishing Co. Pte. Ltd., vol. 6, No. 3, Sep. 1995, pp. 273-282.44Gasteiger, J. et al., "Assessment of the Diversity of Combinatorial Libraries by an Encoding of Molecular Surface Properties," Abstracts of Papers Part 1: 211th ACS National Meeting, Mar. 24-28, 1996, p. 70-CINF.45Geysen, H.M. and Mason, T.J., "Screening Chemically Synthesized PepTide Libraries for Biologically-Relevant Molecules," Bioorganic & Medicinal Chemistry Letters, Pergamon Press, Ltd., vol. 3, No. 3, 1993, pp. 397-404.46Ghose, A.K. et al., "Prediction of Hydrophobic (Lipophilic) Properties of Small Organic Molecules Using Fragmental Methods: An Analysis of ALOGP and CLOGP Methods," Journal of Physical Chemistry, American Chemical Society, vol. 102, No. 21, Mat 21, 1998, pp. 3762-3772.47Gillet, Valerie J. et al., "Selecting Combinatorial Libraries to Optimize Diversity and Physical Properties," Journal of Chemical Information Computer Sciences, American Chemical Society, vol. 39, No. 1, 1999, pp. 169-177.48Gillet, Valerie J. et al., "The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries," Journal of Chemical Information Computer Sciences, American Chemical Society, vol. 37, No. 4, 1997, pp. 731-740.49Gillet, Valerie J., "Background Theory of Molecular Diversity," Molecular Diversity in Drug Design, Kluwer Academic Publishers, 1999, pp. 43-65.50Gobbi, A. et al., "New Leads By Selective Screening of Compounds From Large Databases," Abstracts of Papers Part 1: 213th ACS National Meeting, Apr. 13-17, 1997, p. 67-CINF.51Good, Andrew C. and Lewis, Richard A., "New Methodology for Profiling Combinatorial Libraries and Screening Sets: Cleaning Up the Design Process with HARPick," Journal of Medicinal Chemistry, American Chemical Society, 1997, vol. 40, No. 24, pp. 3926-3936.52Gorse, Dominique and Lahana, Roger, "Functional diversity of compund libraries," Current opinion in chemical biology, Elsevier Science Ltd., Jun. 2000, vol. 4, No. 3, pp. 287-294.53Hall, L.H. and Kier, L.B., "The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure- Property Modeling," Reviews in Computational Chemistry: Advances, VCH Publishers, Inc., 1991, pp. 367-422.54Hassan, Moises et al., "Optimization and visualization of molecular diversity of combinatorial libraries," Molecular Diversity, ESCOM Science Publishers B.V., 1996, vol. 2, pp. 64-74.55Hecht-Nielsen, R., "Replicator Neural Networks for Universal Optimal Source Coding," Science, American Association for the Advancement of Science, vol. 269, Sep. 29, 1995, pp. 1860-1863.56Hotelling, H., "Analysis of a Complex of Statistical Variables into Principal Components," The Journal of Educational Phsychology, Warwick and York, Inc., vol. XXIV, No. 6, Sep. 1933, pp. 417-441.57Hotelling, H., "Analysis of a Complex of Statistical Variables into Principal Components," The Journal of Educational Psychology, Warwick and York, Inc., vol. XXIV, No. 7, Oct. 1933, pp. 498-520.58Houghten, R.A. et al., "The Use of Synthetic Peptide Combinatorial Libraries for the Identification of Bioactive Peptides," Peptide Research, vol. 5 No. 6, 1992, pp. 351-358.59Jamois, Eric A. et al., "Evaluation of Reagent-Based and Product-Based Strategies in the Design of Combinatorial Library Subsets," Journal of Chemical Information and Computer Sciences, American Chemical Society, 2000, vol. 40, no. 1, pp. 63-70.60Johnson, M.A., and Maggiora, G.M., Concepts and Applications of Molecular Similarity, John Wiley and Sons, New York, NY (1990), entire book submitted.61Kearsley, Simon K. et al., "Chemical Similarity Using Physiochemical Property Descriptors," Journal of Chemical Information Computer Science, American Chemical Society, vol. 36, No .1 , 1996, pp. 118-127.62Klopman, G., "Artificial Intelligence Approach to Structure-Activity Studies. Computer Automated Structure Evaluation of Biological Activity of Organic Molecules," Journal of the American Chemical Society, American Chemical Society, vol. 106, No. 24, Nov. 28, 1984, pp. 7315-7321.63Kohonen, T., Self-Organizing Mapes, Springer-Verlag, Heidelberg, Germany (1995), entire book submitted.64Lajiness, M.S. et al., "Implementing Drug Screening Programs Using Molecular Similarity Methods," QSAR: Quantitative Structure-Activity Relationships in Drug Design, Alan R. Liss, Inc., 1989, pp. 173-176.65Leach, Andrew R. and Hann, Michael M., "The in silico world of virtual libraries," Drug discovery today, Elsevier Science Ltd., Aug. 2000, vol. 5, No. 8, pp. 326-336.66Leach, Andrew R. et al., "Implementation of a System for Reagent Selection and Library Enumeration, Profiling, and Design," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1999, vol. 39, No. 6, pp. 1161-1172.67Lee, R.C.T. et al., "A Triangulation MEthod for the Sequential Mapping of Points from N-Space to Two-Space," IEEE Transactions on Computers, The Institute of Electrical and Electronics Engineers, Mar. 1977, pp. 288-292.68Leland, Burton A. et al., "Managing the Combinatorial Explosion," Journal of Chemical Information Computer Science, American Chemical Society, vol. 37, No. 1, 1997, pp. 62-70.69Lewis, Richard A. et al., "Similarity Measures for Rational Set Selection and Analysis of Combinatorial Libraries: The Diverse Property-Derived (DPD) Approach," Journal of Chemical Information Computer Sciences, American Chemical Society, vol. 37, No. 3, 1997, pp. 599-614.70Lipinski, C.A. et al., "Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings," Advanced Drug Delivery Reviews, Elsevier Science B.V., vol. 23, 1997, pp. 3-25.71Lobanov, V.S. and Agrafiotis, D.K., "Intelligent Database Mining Techniques," Abstracts of Papers Part 1: 215th ACS National Meeting, Mar. 29-Apr. 2, 1998, p. 19-COMP.72Lobanov, V.S. et al., "National Selections from Virtual Libraries," Abstracts of Papers Part 1: 217th ACS National Meeting, Mar. 21-25, 1999, p. 181-COMP.73Lobanov, Victor S. and Agrafiotis, Dimitris K., "Stochastic Similarity Selections from Large Combinatorial Libraries," Journal of Chemical Information and Computer Sciences, American Chemical Society, Mar./Arp. 2000, vol. 40, No. 2, pp. 460-470.74Loew, G.H. et al., "Strategies for Indirect Computer-Aided Drug Design," Pharmaceutical Research, Plenum Publishing Corporation, vol. 10, No. 4, 1993, pp. 475-486.75Lykett, C.E., "Imrpoving the Efficiency of Sammon's Nonlinear Mapping by Using Clustering Archetypes," Electronics Letters, The Institute of Electrical Engineers, vol. 14, No. 25, Dec. 7, 1978, pp. 799-800.76Lynch, M.F. et al., "Generic Structure Storage and Retrieval," Journal of Chemical Information and Computer Sciences, American Chemical Society, vol. 25, No. 3, Aug. 1985, pp. 264-270.77Mao, J. and Jain, A.K., "Artificial Neural Networks for Feature Extraction and Multivariate Data Projection," IEEE transactions on Neural Networks, IEEE Neural Networks, vol. 6, No. 2, Mar. 1995, pp. 296-317.78Martin, Eric J. and Critchlow, Roger E., "Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery," Journal of Combinatorial Chemistry, American Chemical Society, vol. 1, No. 1, 1999, pp. 32-45.79Matter, Hans and P�tter, Thorsten, "Comparing 3D Pharmacophore Triplets and 2D Fingerprints for Selecting Diverse Compound Subsets," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1999, vol. 39, No. 6, pp. 1211-1225.80Matter, Hans, "Selecting Optimally Diverse Compounds from Structure Databases: A Validation Study of Two-Dimensional and Three-Dimensional Molecular Descriptors," Journal of Medicinal Chemistry, American Chemical Society, 1997, vol. 40, No. 8, pp. 1219-1229.81Myers, P.L. et al., "Rapid, Reliable Drug Discovery," Today's Chemist At Work, American Chemical Society, vol. 6, No. 7, Jul./Aug. 1997, pp. 46-48, 51 & 53.82Oja, E., "Principal Components, Minor Components, and Linear Neural Networks," Neural Networks, Pergamon Press Ltd., vol. 5, 1992, pp. 927-935.83Oja, E., Subspace Methods of Pattern Recognition, Research Studies Press, Letchworth, England, (1983), entire book submitted.84Pabo, C.O. and Suchanek, E.G., "Computer-Aided Model-Building Strategies for Protein Design," Biochemistry, American Chemical Society, vol. 25, No. 20, 1986, pp. 5987-5991.85Patterson, D.E. et al., "Neighborhood Behavior: A Useful Concept for Validation of 'Molecular Diversity' Descriptors," Journal of Medicinal Chemistry, American Chemical Society, vol. 39, No. 16, 1996, pp. 3049-3059.86Rubner, J. and Tavan, P., "A Self-Organizing Network for Principal-Component Analysis," Europhysics Letters, European Physical Society, vol. 10, No. 7, Dec. 1, 1989, pp. 693-698.87Sadowski, J. et al., "Assessing Similarity and Diversity of Combinatorial Libraries by Spatial Autocorrelation Functions and Neural Networks," Angewandte Chemie, VCH, vol. 34, No .23/24, Jan. 5, 1996, pp. 2674-2677.88Sadowski, Jens and Kubinyi, Hugo, "A Scoring Scheme for Descriminating between Drugd and Nondrugs," Journal of Medicinal Chemistry, American Chemical Society, 1998, vol. 41, No. 18, pp. 3325-3329.89Saudek, V. et al., "Solution Conformation of Endothelin-1 by H NMR, CD, and Molecular Modeling," International Journal of Peptide Protein Res., Munksgaard International Publishers Ltd., vol. 37, No. 3, 1991, pp. 174-179.90Schnur, Dora, "Design and Diversity Analysis of Large Combinatorial Libraries Using Cell-Based Methods," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1999, vol. 39, No. 1, pp. 36-45.91Schuffenhauer, Ansgar et al., "Similarity Searching in Files of Three-Dimensional Chemical Structures: Analysis of the BIOSTER Database Using Two-Dimensional Fingerprints and Molecular Field Descriptors," Journal of Chemical Information and Computer Sciences, American Chemical Society, 2000, vol. 40, No. 2, pp. 295-307.92Sheridan, Robert P. et al., "Chemical Similarity Using Geometric Atom Pair Descriptors," Journal of Chemical Information Computer Sciences, Americna Chemical Society, vol. 36, no. 1, 1996, pp. 128-136.93Singh, J. et al., "Application of Genetic Algorithms to Combinatorial Synthesis: A Computational Approach to Lead Identification and Lead Optimization," Journal of the American Chemical Society, American Chemical Society, vol. 118, No. 5, Feb. 7, 1996, pp. 1669-1676.94Thompson, L.A. and Ellman, J.A., "Synthesis and Applications of Small Molecule Libraries," Chemical Reviews, American Chemical Society, vol. 96, No. 1, Jan./Feb. 1996, pp. 555-600.95Turner, David B. et al., "Rapid Quantification of Molecular Diversity for Selective Database Acquisition," Journal of Chemical Information and Computer Sciences, American Chemical Society, 1997, vol. 37, No. 1, pp. 18-22.96Van Drie, J.H. and Lajiness, M.S., "Approaches to virtual library design," Drug Discovery today, Elsevier Science Ltd., vol. 3, No. 6, Jun. 1998, pp. 274-283.97Walters, W.P. et al., "Virtual screening-an overview," Drug Discovery today, Elsevier Science Ltd., vol. 3, No. 4, Apr. 1998, pp. 160-178.98Wang, Jing and Ramnarayan, Kal, "Toward Designing Drug-Like Libraries: A Novel Computational Approach for Prediction of Durg Feasability of Compounds," Journal of Combinatorial Chemistry, American Chemical Society, Nov./Dec. 1999, vol. 1, No. 6, pp. 524-533.99Willett, Peter et al., "Chemical Similarity Searching," Journal of Chemical Information Computer Sciences, Americna Chemical Society, vol. 38, No. 6, 1998, pp. 983-996.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7174048 *Mar 31, 2005Feb 6, 2007Glickman Jeff BEnergy minimization for classification, pattern recognition, sensor fusion, data compression, network reconstruction and signal processingUS7272262 *Apr 1, 2005Sep 18, 2007Glickman Jeff BEnergy minimization for classification, pattern recognition, sensor fusion, data compression, network reconstruction and signal processingUS7318051 *May 20, 2002Jan 8, 2008Health Discovery CorporationMethods for feature selection in a learning machineUS7483812 *Jul 15, 2005Jan 27, 2009Universitat PotsdamMethod for determining a linear relationship from measured data that can be stored electronicallyUS7523085 *Sep 30, 2005Apr 21, 2009Buzzmetrics, Ltd An Israel CorporationTopical sentiments in electronically stored communicationsUS7623687 *Sep 6, 2005Nov 24, 2009Technion Research & Development Foundation Ltd.Three-dimensional face recognitionUS7624074Oct 30, 2007Nov 24, 2009Health Discovery CorporationMethods for feature selection in a learning machineUS7777743 *Mar 28, 2003Aug 17, 2010Computer Associates Think, Inc.Viewing multi-dimensional data through hierarchical visualizationUS7856136 *Apr 14, 2004Dec 21, 2010Drvision Technologies LlcAnalysis of patterns among objects of a plurality of classesUS7912290Apr 9, 2010Mar 22, 2011Glickman Jeff BEnergy minimization for classification, pattern recognition, sensor fusion, data compression, network reconstruction and signal processingUS8155400Mar 24, 2008Apr 10, 2012Technion Research & Development Foundation L'Facial recognition and the open mouth problemUS8244800 *Nov 13, 2008Aug 14, 2012International Business Machines CorporationVirtual space system, method and programUS8374974Mar 21, 2003Feb 12, 2013Halliburton Energy Services, Inc.Neural network training data selection using memory reduced cluster analysis for field model developmentUS20100040281 *Aug 12, 2008Feb 18, 2010Halliburton Energy Services, Inc.Systems and Methods Employing Cooperative Optimization-Based Dimensionality Reduction* Cited by examinerClassifications U.S. Classification706/20, 706/15International ClassificationG06Q10/00, G06K9/62, G06E1/00, G06F15/18Cooperative ClassificationG06K9/6232, G06N99/005, G06Q10/10, G06K9/6253European ClassificationG06N99/00L, G06K9/62B4, G06K9/62B5, G06Q10/10Legal EventsDateCodeEventDescriptionOct 2, 2013FPAYFee paymentYear of fee payment: 8Sep 30, 2009FPAYFee paymentYear of fee payment: 4Jan 25, 2006ASAssignmentOwner name: JOHNSON & JOHNSON PHARMACEUTICAL RESEARCH AND DEVEFree format text: MERGER;ASSIGNOR:3-DIMENSIONAL PHARMACEUTICALS, INC.;REEL/FRAME:017060/0439Effective date: 20040624Jun 28, 2001ASAssignmentOwner name: 3-DIMENSIONAL PHARMACEUTICALS, INC., PENNSYLVANIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:AGRAFIOTIS, DIMITRIS K.;RASSOKHIN, DMITRII;LOBANOV, VICTOR S.;AND OTHERS;REEL/FRAME:011945/0511;SIGNING DATES FROM 20010419 TO 20010420RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google