Skip to main content
SpringerLink
Log in
Book cover

Systems Biology Application in Synthetic Biology pp 117–147Cite as

Mathematical Chemodescriptors and Biodescriptors: Background and Their Applications in the Prediction of Bioactivity/Toxicity of Chemicals

  • Chapter
  • First Online:

  • 1303 Accesses

Abstract

This chapter reviews results of research carried out by Basak and collaborators during the past four decades in the development of novel mathematical chemodescriptors and omics-based biodescriptors as well as their applications in quantitative structure-activity relationship (QSAR) and quantitative molecular similarity analysis (QMSA) studies related to the prediction of toxicities, bioactivities, and properties of chemicals. For chemodescriptor-based QSAR and QMSA studies, we have used graph theoretical, three-dimensional (3D), and quantum chemical indices. The graph theoretic chemodescriptors fall into two major categories:

  1. (a)

    Numerical invariants defined on simple molecular graphs representing only the adjacency and distance relationship of atoms bonds; such invariants are called topostructural (TS) indices

  2. (b)

    Topological indices derived from weighted molecular graphs, called topochemical (TC) indices.

Collectively, the TS and TC descriptors are known as topological indices (TIs). The set of independent variables used for modeling also includes a group of three-dimensional (3D) molecular descriptors. Semiempirical and various levels of ab initio quantum chemical indices have also been used for hierarchical QSAR (HiQSAR) modeling. Results indicate that in many cases of property-activity/toxicity analyzed by us, a TS + TC combination explains most of the variance in the data.

In the area of quantitative molecular similarity analysis (QMSA), we have used different arbitrary (user-defined) and tailored (property-specific) similarity spaces for analog selection and k-nearest neighbor (KNN)-based property estimation of chemicals from their selected analogs. Preliminary data suggest that tailored spaces outperform arbitrary spaces. Additional research is needed to test the validity of this observation. Rapid clustering of large chemical libraries can be accomplished using calculated TIs, and this approach has promise both for drug discovery and toxicology.

With respect to biodescriptor development, we have mainly applied techniques of statistics, chemometrics, and discrete mathematics in order to calculate invariants of objects associated with proteomics maps. Invariants or vectors calculated from maps derived from normal animals or cells vis-à-vis those treated with drugs and toxicants show that such descriptors are capable of discriminating between maps of control biological systems and those exposed to drugs or xenobiotics. Finally, we discussed the approach of integrated QSAR (I-QSAR) where both computed chemodescriptors and biodescriptors are used for quantitative prediction of bioactivity.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Hardman JG, Limbird LE, Gilman AG (2001) Goodman and Gilman’s the pharmacological basis of therapeutics. McGraw- Hill, New York

    Google Scholar 

  2. Hoffman DJ, Ratner BA, Burton GA Jr, Cairns J Jr (1995) Handbook of ecotoxicology. CRC Press, Boca Raton

    Google Scholar 

  3. Nogrady T (1985) Medicinal chemistry: a biochemical approach. Oxford University Press, New York

    Google Scholar 

  4. Rand G (ed) (1995) Fundamentals of aquatic toxicology: effects, environmental fate and risk assessment, 2nd edn. Taylor and Francis, New York

    Google Scholar 

  5. Primas H (1981) Chemistry, quantum mechanics and reductionism. Springer, Berlin

    Book  Google Scholar 

  6. Woolley RG (1978) Must a molecule have a shape? J Am Chem Soc 100:1073–1078

    Article  CAS  Google Scholar 

  7. Basak SC, Veith GJ, Niemi GD (1991) Predicting properties of molecules using graph invariants. J Math Chem 7:243–272

    Article  CAS  Google Scholar 

  8. Einstein A (1954) Remarks on Bertrand Russell’s theory of knowledge. In: Einstein A (ed) Ideas and opinions. Ed. Carl Seelig, (Based on MEIN WELTBILD, edited by Carl Seelig, and other sources; New translations and revisions by Sonja Bargmann), Crown Publishers, New York, pp 18–24

    Google Scholar 

  9. Bunge M (1973) Method, model and matter. Reidel, Dordrecht

    Book  Google Scholar 

  10. Carhart RE, Smith DH, Venkataraghavan R (1985) Atoms pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25:64–73. doi:10.1021/ci00046a002

    Google Scholar 

  11. Euler I (1736) Solutio problematis ad geometriam situs pertinentis. Comment Acad Sci U Petrop 8:128–140

    Google Scholar 

  12. Sylvester JJ (1878) On an application of the new atomic theory to the graphical representation of the invariants and covariants of binary quantics, with three appendices. Am J Math 1:105–125

    Article  Google Scholar 

  13. http://www.goodreads.com/quotes/926286-chemistry-has-the-same-quickening-and-suggestive-influence-upon-the

  14. Wiener H (1947) Structural determination of paraffin boiling points. J Am Chem Soc 69:17–20

    Article  CAS  PubMed  Google Scholar 

  15. Balasubramanian K, Basak SC (1998) Characterization of isospectral graphs using graph invariants and derived orthogonal parameters. J Chem Inf Comput Sci 38:367–373

    Google Scholar 

  16. Nandy A, Harle M, Basak SC (2006) Mathematical descriptors of DNA sequences: development and application. Arkivoc 9:211–238

    Google Scholar 

  17. Basak SC (2013) Philosophy of mathematical chemistry: a personal perspective. HYLE Int J Philos Chem 19:3–17

    CAS  Google Scholar 

  18. Basak SC (2013) Mathematical descriptors for the prediction of property, bioactivity, and toxicity of chemicals from their structure: a chemical-cum-biochemical approach. Curr Comput Aided Drug Des 9:449–462

    Article  CAS  PubMed  Google Scholar 

  19. Basak SC, Magnuson VR, Niemi GJ, Regal RR (1988) Determining structural similarity of chemicals using graph-theoretic indices. Discrete Appl Math 19:17–44

    Google Scholar 

  20. Lajiness M (1990) Molecular similarity-based methods for selecting compounds for screening. In: Rouvray DH (ed) Computational chemical graph theory. Nova, New York, pp 299–316

    Google Scholar 

  21. Basak SC, Mills D, Gute BD, Balaban AT, Basak K, Grunwald GD (2010) Use of mathematical structural invariants in analyzing, combinatorial libraries: a case study with psoralen derivatives. Curr Comput Aided Drug Des 6:240–251

    Article  CAS  PubMed  Google Scholar 

  22. Basak SC (2014) Molecular similarity and hazard assessment of chemicals: a comparative study of arbitrary and tailored similarity spaces. J Eng Sci Manag Educ 7:178–184

    Google Scholar 

  23. Basak SC (1987) Use of molecular complexity indices in predictive pharmacology and toxicology: a QSAR approach. Med Sci Res 15:605–609

    CAS  Google Scholar 

  24. Raychaudhury C, Ray SK, Ghosh JJ, Roy AB, Basak SC (1984) Discrimination of isomeric structures using information-theoretic topological indices. J Comput Chem 5:581–588

    Article  CAS  Google Scholar 

  25. Balaban AT, Mills D, Ivanciuc O, Basak SC (2000) Reverse wiener indices. Croat Chim Acta 73:923–941

    CAS  Google Scholar 

  26. Nikolic S, Trinajstic N, Amic D, Beslo D, Basak SC (2001) Modeling the solubility of aliphatic alcohols in water. Graph connectivity indices versus line graph connectivity indices. In: Diudea MV (ed) QSAR/QSPR studies by molecular descriptors. Nova, Huntington, pp 63–81

    Google Scholar 

  27. Randic M, Vracko M, Nandy A, Basak SC (2000) On 3-D graphical representation of DNA primary sequences and their numerical characterization. J Chem Inf Comput Sci 40:1235–1244

    Article  CAS  PubMed  Google Scholar 

  28. Basak SC, Gute BD (2008) Mathematical descriptors of proteomics maps: background and applications. Curr Opin Drug Discov Dev 11:320–326

    CAS  Google Scholar 

  29. Hosoya H (1971) Topological index. A newly proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bull Chem Soc Jpn 44:2332–2339

    Article  CAS  Google Scholar 

  30. MolconnZ (2003) Version 4.05. Hall Ass. Consult. Quincy

    Google Scholar 

  31. Basak SC, Harriss DK, Magnuson VR (1988) POLLY v. 2.3. Copyright of the University of Minnesota, USA

    Google Scholar 

  32. Basak SC, Grunwald GD (1993) APProbe. Copyright of the University of Minnesota, USA

    Google Scholar 

  33. Filip PA, Balaban TS, Balaban AT (1987) A new approach for devising local graph invariants: derived topological indices with low degeneracy and good correlation ability. J Math Chem 1:61–83

    Article  CAS  Google Scholar 

  34. Stewart JJP (1990) MOPAC Version 6.00, QCPE #455, Frank J Seiler Research Laboratory, US Air Force Academy, CO

    Google Scholar 

  35. Frisch MJ et al (1998) Gaussian 98 (Revision A.11.2). Gaussian, Inc., Pittsburgh

    Google Scholar 

  36. Auer CM, Nabholz JV, Baetcke KP (1990) Mode of action and the assessment of chemical hazards in the presence of limited data: use of structure-activity relationships (SAR) under TSCA, section 5. Environ Health Perspect 87:183–197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

    Article  CAS  PubMed  Google Scholar 

  38. Todeschini R, Consonni V, Mauri A, Pavan M. (2006) DRAGON – Software for the calculation of molecular descriptors, version 5.4, Talete srl. Milan.

    Google Scholar 

  39. Johnson M, Basak SC, Maggiora G (1988) A characterization of molecular similarity methods for property prediction. Math Comput Mod 11:630–634

    Article  Google Scholar 

  40. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288

    Google Scholar 

  41. Zou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101:1418–1429

    Article  CAS  Google Scholar 

  42. Cook RD, Li B, Chiaromonte F (2010) Envelope models for parsimonious and efficient multivariate linear regression. Stat Sin 20:927–1010

    Google Scholar 

  43. Hawkins DM, Basak SC, Mills D (2003) Assessing model fit by cross-validation. J Chem Inf Comput Sci 3:579–586

    Article  Google Scholar 

  44. Hawkins DM, Basak SC, Mills D (2004) QSARs for chemical mutagens from structure: ridge regression fitting and diagnostics. Environ Toxicol Pharmacol 16:37–44

    Article  CAS  PubMed  Google Scholar 

  45. Basak SC, Mills D, Hawkins DM, Kraker JJ (2007) Proper statistical modeling and validation in QSAR: a case study in the prediction of rat fat-air partitioning. In: Simos TE, Maroulis G (eds) Computation in modern science and engineering, proceedings of the International Conference on Computational Methods in Science and Engineering 2007 (ICCMSE 2007). American Institute of Physics, Melville, pp 548–551

    Google Scholar 

  46. Basak SC, Majumdar S (2016) Current landscape of hierarchical QSAR modeling and its applications: Some comments on the importance of mathematical descriptors as well as rigorous statistical methods of model building and validation. In: Basak SC, Restrepo G, Villaveces JL (ed) Advances in mathematical chemistry and applications, vol 1. Bentham eBooks, Elsevier & Bentham Science Publishers, Sharjah, U. A. E, pp 251–281

    Google Scholar 

  47. Basak SC, Majumdar S (2015) Hierarchical quantitative structure-activity relationships (HiQSARs) for the prediction of physicochemical and toxicological properties of chemicals using computed molecular descriptors, Mol2Net Conference. http://sciforum.net/email/validate/49668c1bf65ab8520f721a84f7d84e05

  48. Majumdar S, Basak SC, Grunwald GD (2013) Adapting interrelated two-way clustering method for quantitative structure-activity relationship (QSAR) modeling of mutagenicity/non-mutagenicity of a diverse set of chemicals. Curr Comput Aided Drug Des 9:463–471

    Article  CAS  PubMed  Google Scholar 

  49. Basak SC, Majumdar S (2015) Prediction of mutagenicity of chemicals from their calculated molecular descriptors: a case study with structurally homogeneous versus diverse datasets. Curr Comput Aided Drug Des 11:117–123

    Article  CAS  PubMed  Google Scholar 

  50. Basak SC, Majumdar S (2015) The importance of rigorous statistical practice in the current landscape of QSAR modelling (editorial). Curr Comput Aided Drug Des 11:2–4

    Article  CAS  PubMed  Google Scholar 

  51. Kraker JJ, Hawkins DM, Basak SC, Natarajan R, Mills D (2007) Quantitative structure-activity relationship (QSAR) modeling of juvenile hormone activity: comparison of validation procedures. Chemometr Intell Lab Syst 87:33–42

    Article  CAS  Google Scholar 

  52. Hawkins DM, Kraker JJ, Basak SC, Mills D (2008) QSPR checking and validation: a case study with hydroxy radical reaction rate constant. SAR QSAR Environ Res 19:525–539

    Article  CAS  PubMed  Google Scholar 

  53. SAS Institute, Inc (1988) In SAS/STAT user guide, release 6.03 edition. Cary

    Google Scholar 

  54. Hoskuldsson A (1995) A combined theory for PCA and PLS. J Chemom 9:91–123

    Article  CAS  Google Scholar 

  55. Hawkins DM, Basak SC, Shi X (2001) QSAR with few compounds and many features. J Chem Inf Comput Sci 41:663–670

    Article  CAS  PubMed  Google Scholar 

  56. Tang C, Zhang L, Zhang A, Ramanathan M (2001) Interrelated two-way clustering: an unsupervised approach for gene expression data analysis. In: Bilof R, Palagi L (eds) Proceedings of BIBE 2001: 2nd IEEE international symposium on bioinformatics and bioengineering, Bethesda, Maryland, November 4–5, 2001. IEEE Computer Society, Los Alamitos, pp 41–48

    Google Scholar 

  57. Basak SC, Magnuson VR, Niemi GJ, Regal RR, Veith GD (1987) Topological indices: their nature, mutual relatedness, and applications. Math Mod 8:300–305

    Article  Google Scholar 

  58. Basak SC, Grunwald GD, Majumdar S (2015) Intrinsic dimensionality of chemical space: characterization and applications, Mol2Net conference. http://sciforum.net/email/validate/49668c1bf65ab8520f721a84f7d84e05

  59. Basak SC (1999) Information theoretic indices of neighborhood complexity and their applications. In: Devillers J, Balaban AT (eds) Topological indices and related descriptors in QSAR and QSPR. Gordon and Breach Science Publishers, Amsterdam, pp 563–593

    Google Scholar 

  60. Randic M (1975) Characterization of molecular branching. J Am Chem Soc 97:6609–6615

    Article  CAS  Google Scholar 

  61. Bonchev D, Trinajstić N (1977) Information theory, distance matrix and molecular branching. J Chem Phys 67:4517–4533

    Article  CAS  Google Scholar 

  62. Hoffmann R, Minkin VI, Carpenter BK (1997) Ockham’s razor and chemistry. HYLE Int J Philos Chem 3:3–28

    Google Scholar 

  63. Katritzky AR, Putrukhin R, Tathan S, Basak SC, Benfenati E, Karelson M, Maran U (2001) Interpretation of quantitative structure-property and -activity relationships. J Chem Inf Comput Sci 41:679–685

    Google Scholar 

  64. Katritzky AR, Putrukhin R, Tathan S, Basak SC, Benfenati E, Karelson M, Maran U (2001) Interpretation of quantitative structure-property and -activity relationships. J Chem Inf Comput Sci 41:679–685

    Google Scholar 

  65. So SS, Karplus M (1997) Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications. J Med Chem 40:4360–4371

    Article  CAS  PubMed  Google Scholar 

  66. Basak SC, Mills D, Mumtaz MM, Balasubramanian K (2003) Use of topological indices in predicting aryl hydrocarbon (Ah) receptor binding potency of dibenzofurans: a hierarchical QSAR approach. Ind J Chem 42A:1385–1391

    CAS  Google Scholar 

  67. Basak SC, Majumdar S (2015) Current landscape of hierarchical QSAR modeling and its applications: some comments on the importance of mathematical descriptors as well as rigorous statistical methods of model building and validation. In: Basak SC, Restrepo G, Villaveces JL (eds) Advances in mathematical chemistry and applications, vol 1. Bentham eBooks, Bentham Science Publishers, pp 251–281

    Google Scholar 

  68. Ben-Dor A, Friedman N, Yakhini Z (2001) Class discovery in gene expression data. In: Proceedings of the fifth annual international conference on computational molecular biology (RECOMB 2001), New York

    Google Scholar 

  69. Gute BD, Basak SC (1997) Predicting acute toxicity of benzene derivatives using theoretical molecular descriptors: a hierarchical QSAR approach. SAR QSAR Environ Res 7:117–131

    Article  CAS  PubMed  Google Scholar 

  70. Gute BD, Grunwald GD, Basak SC (1999) Prediction of the dermal penetration of polycyclic aromatic hydrocarbons (PAHs): a hierarchical QSAR approach. SAR QSAR Environ Res 10:1–15

    Article  CAS  PubMed  Google Scholar 

  71. Basak SC, Mills DR, Balaban AT, Gute BD (2001) Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: a hierarchical QSAR approach. J Chem Inf Comput Sci 41:671–678

    Article  CAS  PubMed  Google Scholar 

  72. Popper K (2005) The logic of scientific discovery. Taylor & Francis e-Library, London and New York

    Google Scholar 

  73. Basak SC, Majumdar S (2015) Two QSAR paradigms- congenericity principle versus diversity begets diversity principle- analyzed using computed mathematical chemodescriptors of homogeneous and diverse sets of chemical mutagens. Mol2Net Conference. http://sciforum.net/email/validate/49668c1bf65ab8520f721a84f7d84e05

  74. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791–4810

    Article  CAS  PubMed  Google Scholar 

  75. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33:445–459

    CAS  PubMed  Google Scholar 

  76. Preparata FP, Shamos MI (1991) Convex hulls: basic algorithms. In: Preparata FP, Shamos MI (eds) Computational geometry: an introduction. Springer, New York, pp 95–148

    Google Scholar 

  77. Worth AP, Bassan A, Gallegos A, Netzeva TI, Patlewicz G, Pavan M, Tsakovska I, Vracko M (2005) The characterisation of (quantitative) structure-activity relationships: preliminary guidance, ECB Report EUR 21866 EN. European Commission, Joint Research Centre, Ispra, p 95

    Google Scholar 

  78. Pharmaceutical Research and Manufacturers of America (2014) Biopharmaceutical research industry profile. Available from: http://www.phrma.org/sites/default/files/pdf/2014_PhRMA_PROFILE.pdf. Accessed on 11 Dec 2015

  79. Santos-Filho OA, Hopfinger AJ, Cherkasov A, de Alencastro RB (2009) The receptor-dependent QSAR paradigm: an overview of the current state of the art. Med Chem (Shariqah) 5:359–366

    Article  CAS  Google Scholar 

  80. Basak SC, Bhattacharjee AK, Vracko M (2015) Big data and new drug discovery: tackling “Big Data” for virtual screening of large compound databases. Curr Comput Aided Drug Des 11:197–201

    Article  CAS  PubMed  Google Scholar 

  81. Crawford MA (1963) The effects of fluoroacetate, malonate and acid-base balance on the renal disposal of citrate. Biochem J 8:115–120

    Article  Google Scholar 

  82. Quastel JH, Wooldridge WR (1928) Some properties of the dehydrogenating enzymes of bacteria. Biochem J 22:689–702

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Basak SC, Grunwald GD (1995) Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study. Chemosphere 31:2529–2546

    Article  CAS  PubMed  Google Scholar 

  84. Reuschenbach P, Silvani M, Dammann M, Warnecke D, Knacker T (2008) ECOSAR model performance with a large test set of industrial chemicals. Chemosphere 71:1986–1995

    Article  CAS  PubMed  Google Scholar 

  85. Ankley GT, Bennett RS, Erickson RJ, Hoff DJ, Hornung W, Johnson RD, Mount DR, Nichols JW, Russom CL, Schmeider PK, Serrano JA, Tietge J, Villeneuve DL (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29:730–741

    Article  CAS  PubMed  Google Scholar 

  86. Ankley GT, Villeneuve DL (2006) The fathead minnow in aquatic toxicology: past, present and future. Aquat Toxicol 78:91–102

    Article  CAS  PubMed  Google Scholar 

  87. Basak SC, Grunwald GD, Host GE, Niemi GJ, Bradbury SP (1998) A comparative study of molecular similarity, statistical and neural network methods for predicting toxic modes of action of chemicals. Environ Toxicol Chem 17:1056–1064

    Article  CAS  Google Scholar 

  88. Russom CL, Bradbury SP, Broderius SJ, Hammermeister DE, Drummond RA (1997) Predicting modes of toxic action from chemical structure: acute toxicity in the fathead minnow (pimephales promelas). Environ Toxicol Chem 16:948–967

    Article  CAS  Google Scholar 

  89. Gute BD, Grunwald GD, Mills D, Basak SC (2001) Molecular similarity based estimation of properties: a comparison of structure spaces and property spaces. SAR QSAR Environ Res 11:363–382

    Article  CAS  PubMed  Google Scholar 

  90. Gute BD, Basak SC, Mills D, Hawkins DM (2002) Tailored similarity spaces for the prediction of physicochemical properties. Internet Electron J Mol Des 1:374–387. http://www.biochempress.com/

    CAS  Google Scholar 

  91. Basak SC, Gute BD, Mills D, Hawkins DM (2003) Quantitative molecular similarity methods in the property/toxicity estimation of chemicals: a comparison of arbitrary versus tailored similarity spaces. J Mol Struct THEOCHEM 622:127–145

    Article  CAS  Google Scholar 

  92. Hamori E, Ruskin J (1983) H Curves, a novel method of representation of nucleotide series especially suited for long DNA sequences. J Biol Chem 258:1318–1327

    CAS  PubMed  Google Scholar 

  93. Gates MA (1986) A simple way to look a DNA. J Theor Biol 119:319–328

    Article  CAS  PubMed  Google Scholar 

  94. Nandy A (1996) Graphical analysis of DNA sequence structure: III. Indications of evolutionary distinctions and characteristics of introns and exons. Curr Sci 70:661–668

    CAS  Google Scholar 

  95. Leong PM, Morgenthaler S (1995) Random walk and gap plots of DNA sequences. Comput Appl Biosci 11:503–507

    CAS  PubMed  Google Scholar 

  96. Randić M, Zupan J, Balaban AT, Vikic-Topic D, Plavsic D (2011) Graphical representation of proteins. Chem Rev 111:790–862

    Article  PubMed  Google Scholar 

  97. Indo-US Workshop on Mathematical Chemistry. http://www.nrri.umn.edu/indousworkshop

  98. Raychaudhury C, Nandy A (1998) Indexation schemes and similarity measures for macromolecular sequences. Paper presented at the Indo-US Workshop on Mathematical Chemistry, Shantiniketan. 9–13 January 1998

    Google Scholar 

  99. Randić M, Vracko M, Nandy A, Basak SC (2000) On 3–D representation of DNA primary sequences. J Chem Inf Comput Sci 40:1235–1244

    Article  PubMed  Google Scholar 

  100. Guo X, Randić M, Basak SC (2001) A novel 2-D graphical representation of DNA sequences of low degeneracy. Chem Phys Lett 350:106–112

    Article  CAS  Google Scholar 

  101. Nandy A, Sarkar T, Basak SC, Nandy P, Das S (2014) Characteristics of influenza HA-NA interdependence determined through a graphical technique. Curr Comput Aided Drug Des 10:285–302

    Article  CAS  PubMed  Google Scholar 

  102. Nandy A, Basak SC (2015) Prognosis of possible reassortments in recent H5N2 epidemic influenza in USA: implications for computer-assisted surveillance as well as drug/vaccine design. Curr Comput Aided Drug Des 11:110–116

    Article  CAS  PubMed  Google Scholar 

  103. Steiner S, Witzmann FA (2000) Proteomics: applications and opportunities in preclinical drug development. Electrophoresis 21:2099–2104

    Article  CAS  PubMed  Google Scholar 

  104. Witzmann FA, Monteiro-Riviere NA (2006) Multi-walled carbon nanotube exposure alters protein expression in human keratinocytes. Nanomedicine Nanotechnol Biol Med 2:158–168

    Article  CAS  Google Scholar 

  105. Basak SC, Gute BD, Monteiro-Riviere N, Witzmann FA (2010) Characterization of toxicoproteomics maps for chemical mixtures using information theoretic approach. In: Mumtaz M (ed) Principles and practice of mixtures toxicology. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 215–232

    Chapter  Google Scholar 

  106. Vracko M, Basak SC, Geiss K, Witzmann FA (2006) Proteomics maps-toxicity relationship of halocarbons studied with similarity index and genetic algorithm. J Chem Inf Model 46:130–136

    Article  CAS  PubMed  Google Scholar 

  107. Randic M, Witzmann FA, Vracko M, Basak SC (2001) On characterization of proteomics maps and chemically induced changes in proteomes using matrix invariants: application to peroxisome proliferators. Med Chem Res 10:456–479

    CAS  Google Scholar 

  108. Basak SC, Gute BD, Witzmann FA (2006) Information-theoretic biodescriptors for proteomics maps: development and applications in predictive toxicology. Conf Proc WSEAS Trans Inf Sci Appl 7:996–1001

    Google Scholar 

  109. Arcos JC (1987) Structure–activity relationships: criteria for predicting the carcinogenic activity of chemical compounds. Environ Sci Technol 21:743–745

    Article  CAS  PubMed  Google Scholar 

  110. Hawkins DM, Basak SC, Kraker JJ, Geiss KT, Witzmann FA (2006) Combining chemodescriptors and biodescriptors in quantitative structure-activity relationship modeling. J Chem Inf Model 46:9–16

    Article  CAS  PubMed  Google Scholar 

  111. Basak SC, Gute BD, Balaban AT (2004) Interrelationship of major topological indices evidenced by clustering. Croat Chem Acta 77:331–344

    CAS  Google Scholar 

  112. Johnson M, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York

    Google Scholar 

  113. Basak SC (2016) Mathematical structural descriptors of molecules and biomolecules: background and applications. In: Basak SC, Restrepo G, Villaveces JL (ed) Advances in mathematical chemistry and applications, vol 1. Bentham eBooks, Elsevier & Bentham Science Publishers, Sharjah, U. A. E. pp 3–23

    Google Scholar 

  114. Zanni R, Galvez-Llompart M, Garcıa-Domenech R, Galvez J (2015) Latest advances in molecular topology applications for drug discovery. Expert Opin Drug Discov 10:1–13

    Article  Google Scholar 

Download references

Acknowledgments

I am thankful to Kanika Basak, Gregory Grunwald, Douglas Hawkins, Brian Gute, Subhabrata Majumdar, Denise Mills, Dilip K. Sinha, Ashesh Nandy, Frank Witzmann, Kevin Geiss, Krishnan Balasubramanian, Ramanathan Natarajan, Gerald J. Niemi, Alexandru T. Balaban, the late Alan Katritzky, Milan Randic, Nenad Trinajstic, Sonja Nikolic, Marjan Vracko, Marjana Novic, Xiaofeng Guo, Terry Neumann, Qianhong Zhu, late Gilman D. Veith, Marissa Harle, Vincent R. Magnuson, Donald K. Harriss, Chandan Raychaudhury, Samar K. Ray and Lester R. Drewes for collaboration in my research.

Author information

Authors and Affiliations

  1. International Society of Mathematical Chemistry, University of Minnesota Duluth-Natural Resources Research Institute, Duluth, MN, USA

    Subhash C. Basak

  2. Department of Chemistry and Biochemistry, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN, 55811, USA

    Subhash C. Basak

Authors
  1. Subhash C. Basak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subhash C. Basak .

Editor information

Editors and Affiliations

  1. Computational and Systems Biology Lab, National Centre for Cell Science, Pune, India

    Shailza Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this chapter

Cite this chapter

Basak, S.C. (2016). Mathematical Chemodescriptors and Biodescriptors: Background and Their Applications in the Prediction of Bioactivity/Toxicity of Chemicals. In: Singh, S. (eds) Systems Biology Application in Synthetic Biology. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2809-7_10

Download citation

Publish with us

Policies and ethics

Search

Navigation