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Demonstrates that kernel entropy maximization improved robustness and prediction accuracy on heterogenous microarray data, •
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Fujibuchi W, Kato T: Classification of heterogeneous microarray data by maximum entropy kernel. BMC Bioinform (2007) 8:267. • Demonstrates that kernel entropy maximization improved robustness and prediction accuracy on heterogenous microarray data.
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Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A: New methods for ligand-based virtual screening: Use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model (2006) 46(2):462-470. •• Describes how group fusion (the use of several bioactive reference structures and the assumption that nearest neighbors in similarity space are also active) was shown to improve upon existing molecular similarity searching methods.
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Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A: New methods for ligand-based virtual screening: Use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model (2006) 46(2):462-470. •• Describes how group fusion (the use of several bioactive reference structures and the assumption that nearest neighbors in similarity space are also active) was shown to improve upon existing molecular similarity searching methods.
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Demonstrates that the fusion of traditional chemical descriptors with biological descriptors has improved toxicity predictions, •
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Hawkins DM, Basak SC, Kraker J, Geiss KT, Witzmann FA: Combining chemodescriptors and biodescriptors in quantitative structure-activity relationship modeling. J Chem Inf Model (2006) 46(1):9-16. • Demonstrates that the fusion of traditional chemical descriptors with biological descriptors has improved toxicity predictions.
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33646561524
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Complex graph matrix representations and characterizations of proteomic maps and chemically induced changes to proteomes
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Describes the classification of molecules through hierarchal clustering and bioassay inhibition profiling, •
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Fliri AF, Loging WT, Thadeio PF, Volkmann RA: Biospectra analysis: Model proteome characterization for linking molecular structure and biological response. J Med Chem (2005) 48(22):6918-6925. • Describes the classification of molecules through hierarchal clustering and bioassay inhibition profiling.
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Yildirim MA, Goh K-I, Cusick ME, Barabási A-L, Vidal M: Drug-target network. Nat Biotechnol (2007) 25(10):1119- 1126. • Demonstrates how FDA-approved drugs and their targets were used to construct a bipartite graph of protein-drug interactions, which was then decomposed into two network projections: a drug network and a complementary target protein network. Strong local clustering of drugs and a recent slow trend toward greater promiscuity and more diversified targets was noted.
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23
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Functional cartography of complex metabolic networks
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Describes the construction of a cartographic representation of metabolic networks for 12 organisms, which showed a modular structure with metabolites connecting different modules being more strongly conserved than within-module hubs, •
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Guimerà R, Nunes Amaral LA: Functional cartography of complex metabolic networks. Nature (2005) 433(7028):895-900. • Describes the construction of a cartographic representation of metabolic networks for 12 organisms, which showed a modular structure with metabolites connecting different modules being more strongly conserved than within-module hubs.
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Laederach A: Informatics challenges in structured RNA. Briefings Bioinform (2007) 8(5):294-303. • Points of interest include folding dynamics and nucleic acid recognition in RNA interference.
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Laederach A: Informatics challenges in structured RNA. Briefings Bioinform (2007) 8(5):294-303. • Points of interest include folding dynamics and nucleic acid recognition in RNA interference.
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Recent developments of the Chemistry Development Kit (CDK) - An open-source Java library for chemo- and bioinformatics
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Describes new biologically and chemically relevant features of the opensource software toolkit CDK, •
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Steinbeck C, Hoppe C, Kuhn S, Floris M, Guha R, Willighagen EL: Recent developments of the Chemistry Development Kit (CDK) - An open-source Java library for chemo- and bioinformatics. Curr Pharm Design (2006) 12(17):2111-2120. • Describes new biologically and chemically relevant features of the opensource software toolkit CDK.
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Ballester PJ, Richards WG: Ultrafast shape recognition for similarity search in molecular databases. Proc R Soc A (2007) 463(2081):1307-1321.
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27
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Ultrafast shape recognition to search compound databases for similar molecular shapes
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USR is a rapid, alignment-free similarity measure based on molecular shape moments, which demonstrated good performance at shape classification, •
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Ballester PJ, Richards WG: Ultrafast shape recognition to search compound databases for similar molecular shapes. J Comput Chem (2007) 28(10):1711-1723. • USR is a rapid, alignment-free similarity measure based on molecular shape moments, which demonstrated good performance at shape classification.
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28
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Lingos, finite state machines and fast similarity searching
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Introduces the Lingo method, based on encoding molecular structure into canonical SMILES strings, which were decomposed into a set of overlapping character sub-strings. Molecular similarity assessment is therefore reduced to comparing Lingos, •
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Grant JA, Haigh JA, Pickup BT, Nicholls A, Sayle RA: Lingos, finite state machines and fast similarity searching. J Chem Inf Model (2006) 46(5):1912-1918. • Introduces the Lingo method, based on encoding molecular structure into canonical SMILES strings, which were decomposed into a set of overlapping character sub-strings. Molecular similarity assessment is therefore reduced to comparing Lingos.
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J Chem Inf Model
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Demonstrates the use of a novel similarity score: the normalized compression distance as a complementary alternative to Tanimoto similarity, ••
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Bender A, Glen RC: A discussion of measures of enrichment in virtual screening: Comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model (2005) 45(5):1369-1375. • Discusses the efficacy and performance of different molecular descriptors in virtual high-throughput screens. Compared with simple molecular descriptors (such as atom and bond counts), 2D fingerprints were found to enrich only by a factor of two, with often no improvement seen in VHTS screening modes.
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Provides a good introductory review of molecular similarity and QSARs, •
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Presents new developments in molecular mining, structure-activity relationships, chemogenomics and de novo and fragment-based molecular design, •
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Ashworth J, Havranek JJ, Duarte CM, Sussman D, Monnat RJ Jr, Stoddard BL, Baker D: Computational redesign of endonuclease DNA binding and cleavage specificity. Nature (2006) 441(7093):656-659. • Demonstrates the remarkable increase in activity of an enzyme and recognition site through computational redesign using a physically realistic atomic-level force field.
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Ashworth J, Havranek JJ, Duarte CM, Sussman D, Monnat RJ Jr, Stoddard BL, Baker D: Computational redesign of endonuclease DNA binding and cleavage specificity. Nature (2006) 441(7093):656-659. • Demonstrates the remarkable increase in activity of an enzyme and recognition site through computational redesign using a physically realistic atomic-level force field.
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Sukumar N, Lawrence CE, Breneman CM, Bennett KP, Vitol I: Fuzzy bar code representations of DNA-protein interactions. Division of Computers in Chemistry, 226th American Chemical Society National Meeting, New York, NY, USA (2003):COMP 19.
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Sukumar N, Lawrence CE, Breneman CM, Bennett KP, Vitol I: Fuzzy bar code representations of DNA-protein interactions. Division of Computers in Chemistry, 226th American Chemical Society National Meeting, New York, NY, USA (2003):COMP 19.
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Sukumar N, Lawrence CE, Breneman CM, Bennett KP, Vitol I: Dixel modeling of gene expression. Division of Chemical Information, 227th American Chemical Society National Meeting, Anaheim, CA, USA (2004):CINF 1.
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Sukumar N, Lawrence CE, Breneman CM, Bennett KP, Vitol I: Dixel modeling of gene expression. Division of Chemical Information, 227th American Chemical Society National Meeting, Anaheim, CA, USA (2004):CINF 1.
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Transferable atom equivalent multi-centered multipole expansion method
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Whitehead CE, Breneman CM, Sukumar N, Ryan MD: Transferable atom equivalent multi-centered multipole expansion method. J Comput Chem (2003) 24(4):512-529.
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Song M, Breneman CM, Bi J, Sukumar N, Bennett KP, Cramer S, Tugcu N: Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Comput Sci (2002) 42(6):1347-1357.
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Matta CF, Boyd RJ Eds, Wiley-VCH Verlag GmbH & Co KGaA, Weinheim, Germany
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Sukumar N, Breneman CM: QTAIM in drug discovery and protein modeling. In: The Quantum Theory of Atoms in Molecules: From Solid State to DNA and Drug Design. Matta CF, Boyd RJ (Eds), Wiley-VCH Verlag GmbH & Co KGaA, Weinheim, Germany (2007):471-498.
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Dixel: Sukumar N, Martin JG, Breneman CM, Rensselar Exploratory Center for Cheminformatics Research, Troy, NY, USA (2006). reccr. chem.rpi.edu/Software/Dixel/dixel-index.html
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Barbano PE, Spivak M, Flajolet M, Nairn AC, Greengard P, Greengard L: A mathematical tool for exploring the dynamics of biological networks. Proc Nat Acad Sci USA (2007) 104(49):19169-19174. • Probes critical points of function in biological networks by using kinetic model parameters as input, and Laplacian eigenvalues for data reduction.
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Barbano PE, Spivak M, Flajolet M, Nairn AC, Greengard P, Greengard L: A mathematical tool for exploring the dynamics of biological networks. Proc Nat Acad Sci USA (2007) 104(49):19169-19174. • Probes critical points of function in biological networks by using kinetic model parameters as input, and Laplacian eigenvalues for data reduction.
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101
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Jennings A, Tennant M: Selection of molecules based on shape and electrostatic similarity: Proof of concept of 'electroforms'. J Chem Inf Model (2007) 47(5):1829-1838.
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Batista J, Bajorath J: Chemical database mining through entropy-based molecular similarity assessment of randomly generated structural fragment populations. J Chem Inf Model (2007) 47(1):59-68. • Describes a class-independent fragmentation scheme, based on the generation of random molecular fragment populations. This was devised and employed with an information entropy-based molecular similarity metric to search for and identify active molecules. Performance was comparable to similarity searching with 2D fingerprints and multiple active reference structures were reported.
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Batista J, Bajorath J: Chemical database mining through entropy-based molecular similarity assessment of randomly generated structural fragment populations. J Chem Inf Model (2007) 47(1):59-68. • Describes a class-independent fragmentation scheme, based on the generation of random molecular fragment populations. This was devised and employed with an information entropy-based molecular similarity metric to search for and identify active molecules. Performance was comparable to similarity searching with 2D fingerprints and multiple active reference structures were reported.
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103
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Nikolova N, Jaworska J: Approaches to measure chemical similarity - A review. QSAR Comb Sci (2003) 22(9/10):1006-1026. • Provides an extensive review of molecular similarity measures in the context of QSAR.
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Breneman CM, Sukumar N, Embrechts MJ, Bennett KP, Sundling CM, Krein M, Hepburn T: Realizing prospective QSAR through data fusion and modern descriptors. Division of Computers in Chemistry, 234th American Chemical Society National Meeting, Boston, MA, USA (2007):COMP 174.
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Division of Computers in Chemistry, 234th American Chemical Society National Meeting
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Breneman, C.M.1
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