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Computational Systems Chemical Biology

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Chemoinformatics and Computational Chemical Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 672))

Abstract

There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447–450, 2007).

The overarching goal of computational SCB is to develop tools for integrated chemical–biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug–target–clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

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Notes

  1. 1.

    The symposium “Systems chemical biology: Integrating chemistry and biology for network models” was organized at the 236th ACS National Meeting in Philadelphia, August 17–21, 2008; it was sponsored by CINF and co-sponsored by four other ACS divisions (COMP, MEDI, HEALTH, and BIOT).

  2. 2.

    KEGG (http://www.genome.jp/kegg/) has the following entry points: PATHWAY, the KEGG pathway maps for biological processes; BRITE, the KEGG functional hierarchies of biological systems; GENES: the KEGG gene catalogs and ortholog relations in complete genomes; and LIGAND, the KEGG chemical compounds, drugs, glycans, and reactions.

  3. 3.

    BioCyc includes MetaCyc, a database of nonredundant, experimentally elucidated metabolic pathways, that can be queried by Pathway, Reaction and Compound, http://metacyc.org/, and the Open Chemical Database, a collection of associated metabolites, http://biocyc.org/open-compounds.shtml.

  4. 4.

    Biocarta is a commercially-sponsored “open source” forum that integrates emerging proteomic information from the scientific community and depicts inter-molecular interactions via dynamic graphical models. http://www.biocarta.com/genes/index.asp.

References

  1. Voit E., Neves A. R., and Santos H. (2006) The intricate side of systems biology. Proc Natl Acad Sci U S A 103, 9452–9457.

    Article  PubMed  CAS  Google Scholar 

  2. Kell D. B. (2006) Theodor Bucher Lecture. Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells. Delivered on 3 July 2005 at the 30th FEBS Congress and the 9th IUBMB conference in Budapest. FEBS J 273, 873–894.

    Google Scholar 

  3. Blinov M. L., Faeder J. R., Goldstein B., and Hlavacek W. S. (2006) A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. Biosystems 83, 136–151.

    Article  PubMed  CAS  Google Scholar 

  4. Ochi H. and Westerfield M. (2007) Signaling networks that regulate muscle development: lessons from zebrafish. Dev Growth Differ 49. 1–11.

    Article  PubMed  CAS  Google Scholar 

  5. Brandman O., Ferrell J. E., Jr., Li R., and Meyer T. (2005) Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496–498.

    Article  PubMed  CAS  Google Scholar 

  6. Covert M. W., Knight E. M., Reed J. L., Herrgard M. J., and Palsson B. O. (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96.

    Article  PubMed  CAS  Google Scholar 

  7. Oprea T. I., Tropsha A, Faulon J. L., and Rintoul M. D. (2007) Systems chemical biology. Nat Chem Biol 3, 447–450.

    Article  PubMed  CAS  Google Scholar 

  8. Mestres J., Martin-Couce L., Gregori-Puigjane E., Cases M., and Boyer S. (2006) Ligand-based approach to in silico pharmacology: nuclear receptor profiling. J Chem Inf Model 46, 2725–2736.

    Article  PubMed  CAS  Google Scholar 

  9. Paolini G. V., Shapland R. H., van Hoorn W. P., Mason J. S., and Hopkins A. L. (2006) Global mapping of pharmacological space. Nat Biotechnol 24, 805–815.

    Article  PubMed  CAS  Google Scholar 

  10. Morphy R. and Rankovic Z. (2007) Fragments, network biology and designing multiple ligands. Drug Discov Today 12, 156–160.

    Article  PubMed  CAS  Google Scholar 

  11. Loging W., Harland L., and Williams-Jones B. (2007) High-throughput electronic biology: mining information for drug discovery. Nat Rev Drug Discov 6, 220–230.

    Article  PubMed  CAS  Google Scholar 

  12. Austin C. P., Brady L. S., Insel T. R., and Collins F. S. (2004) NIH Molecular Libraries Initiative. Science 306, 1138–1139.

    Article  PubMed  CAS  Google Scholar 

  13. PubChem. (2009) http://pubchem.ncbi.nlm.nih.gov/.

  14. Lipinski C. A., Lombardo F., Dominy B. W., and Feeney P. J. (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46, 3–26.

    Article  PubMed  CAS  Google Scholar 

  15. Brown F. (2005) Editorial opinion: chemoinformatics – a ten year update. Curr Opin Drug Discov Devel 8, 298–302.

    PubMed  CAS  Google Scholar 

  16. Olsson T. and Oprea T. I. (2001) Cheminformatics: a tool for decision-makers in drug discovery. Curr Opin Drug Discov Devel 4, 308–313.

    PubMed  CAS  Google Scholar 

  17. Willett P. (2008) A bibliometric analysis of the literature of chemoinformatics. Aslib Proc 60, 4–17.

    Article  Google Scholar 

  18. Fliri A. F., Loging W. T., Thadeio P. F., and Volkmann R. A. (2005) Biological spectra analysis: linking biological activity profiles to molecular structure. Proc Natl Acad Sci U S A 102, 261–266.

    Article  PubMed  CAS  Google Scholar 

  19. Schreiber S. L. (2005) Small molecules: the missing link in the central dogma. Nat Chem Biol 1, 64–66.

    Article  PubMed  CAS  Google Scholar 

  20. Varnek A. and Tropsha A. (2008) Cheminformatics Approaches to Virtual Screening. London: RSC.

    Book  Google Scholar 

  21. Danhof M., de Lange E. C., Della Pasqua O. E., Ploeger B. A., and Voskuyl R. A. (2008) Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research. Trends Pharmacol Sci 29, 186–191.

    Article  PubMed  CAS  Google Scholar 

  22. Schmidt S., Barbour A., Sahre M., Rand K. H., and Derendorf H. (2008) PK/PD: new insights for antibacterial and antiviral applications. Curr Opin Pharmacol 8, 549–556.

    Article  PubMed  CAS  Google Scholar 

  23. Oprea T. and Tropsha A. (2006) Target, chemical and bioactivity databases – integration is key. Drug Discov Today 3, 357–365.

    Google Scholar 

  24. de Jong L. A., Uges D. R., Franke J. P., and Bischoff R. (2005) Receptor-ligand binding assays: technologies and applications. J Chromatogr B Analyt Technol Biomed Life Sci 829, 1–25.

    Article  PubMed  Google Scholar 

  25. SciFinder: American Chemical Society, CAS online/SciFinder. (2009) http://www.cas.org/SCIFINDER/.

  26. MDDR.SYMYX technologies. (2009) http://www.mdl.com/products/knowledge/drug_data_report/index.jsp.

  27. Olah M., Rad R., Ostopovici L., Bora A., Hadaruga N., Hadaruga D. et al. (2007) WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery. In: Schreiber S. L., Kapoor T. M., Weiss G. (Eds). Chemical Biology: From Small Molecules to Systems Biology and Drug Design. New York: Wiley-VCH, 760–786.

    Google Scholar 

  28. Brunton L, Lazo J, Parker K.(Eds). (2006) Goodman and Gilman’s The Pharmacological Basis of Therapeutics. 11th ed. New York: McGraw-Hill; 1984 pp.

    Google Scholar 

  29. Physicians’ Desk Reference (2009) 63rd ed. PDR., Montvale, NJ, Thomson Reuters, 3315 pp.

    Google Scholar 

  30. FDA Approved Drug Products (2009) Available from http://www.accessdata.fda.gov/scripts/cder/ob/default.cfm

  31. Vaz R. and Klabunde T. (2008) Antitargets: prediction and prevention of drug side effects. In: Methods and Principles in Medicinal Chemistry. Weinheim: Wiley-VCH.

    Google Scholar 

  32. Chen X., Liu M., and Gilson M. K. (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen 4, 719–725.

    Article  PubMed  CAS  Google Scholar 

  33. Strausberg R. L. and Schreiber S. L. (2003) From knowing to controlling: a path from genomics to drugs using small molecule probes. Science 300, 294–295.

    Article  PubMed  CAS  Google Scholar 

  34. Kanehisa M., Goto S., Hattori M., Aoki-Kinoshita K. F., Itoh M., Kawashima S. et al. (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34(Database issue), D354–D357.

    Article  PubMed  CAS  Google Scholar 

  35. Snyder K. A., Feldman H. J., Dumontier M., Salama J. J., and Hogue C. W. (2006) Domain-based small molecule binding site annotation. BMC Bioinformatics 7, 152.

    Article  PubMed  Google Scholar 

  36. Garcia-Serna R., Ursu O., Oprea T., and Mestres J. (2010) iPHACE: integrative navigation in pharmacological space. Bioinformatics. 26, 985–986.

    Google Scholar 

  37. Okuno Y., Yang J., Taneishi K., Yabuuchi H., and Tsujimoto G. (2006) GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res 34 (Database issue), D673–D677.

    Article  PubMed  CAS  Google Scholar 

  38. KEGG. (2009.) http://www.genome.jp/kegg/.

  39. Berman H. M., Westbrook J., Feng Z., Gilliland G., Bhat T. N., Weissig H. et al. (2000) The Protein Data Bank. Nucleic Acids Res 28, 235–242.

    Article  PubMed  CAS  Google Scholar 

  40. ChemSpider. (2009) ChemSpider. http://www.chemspider.com.

  41. Amidon G. L., Lennernas H., Shah V. P., and Crison J. R. (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res 12, 413–420.

    Article  PubMed  CAS  Google Scholar 

  42. Wu C. Y. and Benet L. Z. (2005) Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res 22, 11–23.

    Article  PubMed  CAS  Google Scholar 

  43. Benet L. Z., Amidon G. L., Barends D. M., Lennernas H., Polli J. E., Shah V. P. et al. (2008) The use of BDDCS in classifying the permeability of marketed drugs. Pharm Res 25, 483–488.

    Article  PubMed  CAS  Google Scholar 

  44. Drews J. and Ryser S. (1997) The role of innovation in drug development. Nat Biotechnol 15, 1318–1319.

    Article  PubMed  CAS  Google Scholar 

  45. Imming P., Sinning C., and Meyer A. (2006) Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov 5, 821–834.

    Article  PubMed  CAS  Google Scholar 

  46. Overington J. P., Al Lazikani B., and Hopkins A. L. (2006) How many drug targets are there? Nat Rev Drug Discov 5, 993–996.

    Article  PubMed  CAS  Google Scholar 

  47. Hopkins A. L. and Groom C. R. (2002) The druggable genome. Nat Rev Drug Discov 1, 727–730.

    Article  PubMed  CAS  Google Scholar 

  48. Wishart D. S., Knox C., Guo A. C., Shrivastava S., Hassanali M., Stothard P. et al. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34 (Database issue), D668–D672.

    Article  PubMed  CAS  Google Scholar 

  49. Campillos M., Kuhn M., Gavin A. C., Jensen L. J., and Bork P. (2008) Drug target identification using side-effect similarity. Science 321, 263–266.

    Article  PubMed  CAS  Google Scholar 

  50. Brooijmans N. and Kuntz I. D. (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32, 335–373.

    Article  PubMed  CAS  Google Scholar 

  51. Kitchen D. B., Decornez H., Furr J. R., and Bajorath J. (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3, 935–949.

    Article  PubMed  CAS  Google Scholar 

  52. Kuntz I. D., Blaney J. M., Oatley S. J, Langridge R., and Ferrin T. E. (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161, 269–288.

    Article  PubMed  CAS  Google Scholar 

  53. Wlodawer A. and Vondrasek J. (1998) Inhibitors of HIV-1 protease: a major success of structure-assisted drug design. Annu Rev Biophys Biomol Struct 27, 249–284.

    Article  PubMed  CAS  Google Scholar 

  54. Wong C. F. and McCammon J. A. (2003) Protein flexibility and computer sided drug design. Annual Rev Pharmacol Toxicol 43, 31–45.

    Article  CAS  Google Scholar 

  55. Taylor R. D., Jewsbury P. J. and Essex J. W. (2002) A review of protein-small molecule docking methods. J Comput Aided Mol Des 16, 151–166.

    Article  PubMed  CAS  Google Scholar 

  56. Muegge I. (2003) Selection criteria for drug-like compounds. Med Res Rev 23, 302–321.

    Article  PubMed  CAS  Google Scholar 

  57. Cho S. J., Zheng W., and Tropsha A. (1998) Rational combinatorial library design. 2. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse QSAR approaches. J Chem Inf Comput Sci 38, 259–268.

    Article  PubMed  CAS  Google Scholar 

  58. Jones G., Willett P., Glen R. C., Leach A. R., and Taylor R. (1997) Development and validation of a genetic algroithm for flexible docking. J Mol Biol 267, 727–748.

    Article  PubMed  CAS  Google Scholar 

  59. Warren G. L., Andrews C. W., Capelli A. M., Clarke B., LaLonde J., Lambert M. H. et al. (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49, 5912–5931.

    Article  PubMed  CAS  Google Scholar 

  60. Keiser M. J., Roth B. L., Armbruster B. N., Ernsberger P., Irwin J. J., and Shoichet B. K. (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25, 197–206.

    Article  PubMed  CAS  Google Scholar 

  61. Keiser M. J., Setola V., Irwin J. J., Laggner C., Abbas A. I., Hufeisen S. J. et al. (2009) Predicting new molecular targets for known drugs. Nature 462, 175–181.

    Article  PubMed  CAS  Google Scholar 

  62. Tropsha A. (2005) Application of predictive QSAR models to database mining. In: Oprea T. (Ed.). Cheminformatics in Drug Discovery. Wiley-VCH, Weinheim, 437–455.

    Google Scholar 

  63. Tropsha A. and Golbraikh A. (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13, 3494–3504.

    Article  PubMed  CAS  Google Scholar 

  64. Golbraikh A. and Tropsha A. (2002) Beware of q2! J Mol Graph Model 20, 269–276.

    Article  PubMed  CAS  Google Scholar 

  65. 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. Quant Struct Act Relat Comb Sci 22, 69–77.

    CAS  Google Scholar 

  66. Sachs, L. (1984) Applied Statistics: A Handbook of Techniques. 2nd ed, New York: Springer-Verlag.

    Google Scholar 

  67. Golbraikh A., Shen M., Xiao Z., Xiao Y. D., Lee K. H, and Tropsha A. (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17, 241–253.

    Article  PubMed  CAS  Google Scholar 

  68. Irwin J. J. and Shoichet B. K. (2005) ZINC – a free database of commercially available compounds for virtual screening. J Chem Inf Model 45, 177–182.

    Article  PubMed  CAS  Google Scholar 

  69. Medina-Franco J. L., Golbraikh A., Oloff S., Castillo R., and Tropsha A. (2005) Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the k nearest neighbor method and QSAR-based database mining. J Comput Aided Mol Des 19, 229–242.

    Article  PubMed  CAS  Google Scholar 

  70. Oloff S., Mailman R. B., and Tropsha A. (2005) Application of validated QSAR models of D1 dopaminergic antagonists for database mining. J Med Chem 48, 7322–7332.

    Article  PubMed  CAS  Google Scholar 

  71. Shen M., Beguin C., Golbraikh A., Stables J. P., Kohn H., and Tropsha A. (2004) Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. J Med Chem 47, 2356–2364.

    Article  PubMed  CAS  Google Scholar 

  72. Zhang S., Wei L., Bastow K., Zheng W., Brossi A., Lee K. H. et al. (2007) Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des 21, 97–112.

    Article  PubMed  CAS  Google Scholar 

  73. Tang H., Wang X. S., Huang X. P., Roth B. L., Butler K. V., Kozikowski A. P. et al. (2009) Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation. J Chem Inf Model 49, 461–476.

    Article  PubMed  CAS  Google Scholar 

  74. Hoops S., Sahle S., Gauges R., Lee C., Pahle J., Simus N. et al. (2006) COPASI – a COmplex PAthway SImulator. Bioinformatics 22, 3067–3074.

    Article  PubMed  CAS  Google Scholar 

  75. Loew L. M. and Schaff J. C. (2001) The virtual cell: a software environment for computational cell biology. Trends Biotechnol 19, 401–406.

    Article  PubMed  CAS  Google Scholar 

  76. Slepoy A., Thompson A. P., and Plimpton S. J. (2008) A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks. J Chem Phys 128, 205101.

    Article  PubMed  Google Scholar 

  77. Salis H., Sotiropoulos V., and Kaznessis Y. N. (2006) Multiscale Hy3S: hybrid stochastic simulation for supercomputers. BMC Bioinformatics 7, 93.

    Article  PubMed  Google Scholar 

  78. Tomita M., Hashimoto K., Takahashi K., Shimizu T. S., Matsuzaki Y., Miyoshi F. et al. (1999) E-CELL: software environment for whole-cell simulation. Bioinformatics 15, 72–84.

    Article  PubMed  CAS  Google Scholar 

  79. Yang C. R., Shapiro B. E., Mjolsness E. D., and Hatfield G. W. (2005) An enzyme mechanism language for the mathematical modeling of metabolic pathways. Bioinformatics 21, 774–780.

    Article  PubMed  CAS  Google Scholar 

  80. May E. E. and Schiek R. L. (2009) BioXyce: an engineering platform for the study of cellular systems. IET Syst Biol 3, 77–89.

    Article  PubMed  CAS  Google Scholar 

  81. Schiek R. L. and May E. E. (2006) Xyce Parallel Electronic Simulator: Biological Pathway Modeling and Simulation. Albuquerque, NM, Sandia National Laboratories, Report No. SAND2006-1993p.

    Google Scholar 

  82. Eldred M. S., Adams B. M., Haskell K., Bohnhoff W. J., Eddy J. P., Gay D. M. et al. (2008) DAKOTA: A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis. Version 4.2 Users Manual. SAND2006-6337.

    Google Scholar 

  83. Oishi M. and May E. E. (2007) Addressing biological circuit simulation accuracy: Reachability for parameter identification and initial conditions. Bethesda, MD. IEEE/NIH BISTI Life Science Systems and Application Workshop.

    Google Scholar 

  84. Chang A., Scheer M., Grote A., Schomburg I., and Schomburg D. (2009) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res 37(Database issue), D588–D592.

    Article  PubMed  CAS  Google Scholar 

  85. Schomburg I., Chang A., Ebeling C., Gremse M., Heldt C., Huhn G. et al. (2004) BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res 32(Database issue), D431–D433.

    Article  PubMed  CAS  Google Scholar 

  86. Wayne L. G. and Sohaskey C. D. (2001) Nonreplicating persistence of Mycobacterium tuberculosis. Annu Rev Microbiol 55, 139–163.

    Article  PubMed  CAS  Google Scholar 

  87. Wayne L. G. and Lin K. Y. (1982) Glyoxylate metabolism and adaptation of Mycobacterium tuberculosis to survival under anaerobic conditions. Infect Immun 37, 1042–1049.

    PubMed  CAS  Google Scholar 

  88. Goodford P. J. (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28, 849–857.

    Article  PubMed  CAS  Google Scholar 

  89. Mdluli K. and Spigelman M. (2006) Novel targets for tuberculosis drug discovery. Curr Opin Pharmacol 6, 459–467.

    Article  PubMed  CAS  Google Scholar 

  90. Ariens E. J., Simonis A. M., and De Groot W. M. (1955) Affinity and intrinsic-activity in the theory of competitive- and non-competitive inhibition and an analysis of some forms of dualism in action. Arch Int Pharmacodyn Ther 100, 298–322.

    PubMed  CAS  Google Scholar 

  91. Olah M., Oprea T. I. (2006) Bioactivity Databases. In: J. B. Taylor, D. J. Triggle (Eds.). Comprehensive Medicinal Chemistry II Vol. 3. Elsevier, Oxford, 293–313.

    Google Scholar 

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Acknowledgments

The authors would like to acknowledge the support of their studies from NIH (grants R01GM066940 and R21GM076059 supporting AT and 1U54MH084690-01 supporting TIO).

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, School of Medicine, University of New Mexico, Albuquerque, NM, USA

    Tudor I. Oprea & Andrei Leitão

  2. Sandia National Laboratories, Complex Systems and Discrete Mathematics, Albuquerque, NM, USA

    Elebeoba E. May

  3. Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Alexander Tropsha

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  1. Tudor I. Oprea
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Editors and Affiliations

  1. , Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universitä, Dahlmannstr. 2, Bonn, 53113, Germany

    Jürgen Bajorath

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Oprea, T.I., May, E.E., Leitão, A., Tropsha, A. (2011). Computational Systems Chemical Biology. In: Bajorath, J. (eds) Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology, vol 672. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-839-3_18

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