Molecular Diversity

, Volume 10, Issue 1, pp 39–79 | Cite as

Molecular similarity and diversity in chemoinformatics: From theory to applications

  • Ana G. Maldonado
  • J. P. Doucet
  • Michel Petitjean
  • Bo-Tao Fan
Reveiw

Abstract

This review is dedicated to a survey on molecular similarity and diversity. Key findings reported in recent investigations are selectively highlighted and summarized. Even if this overview is mainly centered in chemoinformatics, applications in other areas (pharmaceutical and medical chemistry, combinatorial chemistry, chemical databases management, etc.) are also introduced. The approaches used to define and descript the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed in the first part of this review. We introduce, in the second and third parts, the descriptions and analyses of different methods and techniques. Finally, current applications and problems are enumerated and discussed in the last part.

Keywords

chemoinformatics classification methods clustering methods combinatorial chemistry compound selection descriptors drug design high throughput screening library design molecular diversity molecular similarity partitioning selection methods similar property principle validation methods 

Abbreviations:

AAB

Advanced Algorithm Builder

ADMET

absorption, distribution, metabolism, excretion and toxicity

ANN

Artificial Neural Networks

BCUT

Burden CAS University of Texas (topological descriptors)

CART

Classification And Regression Tree

CAS

Chemical Abstract Service (American Chemical Society)

CLIP program

Candidate Ligand Identification Program

CODESSA

COmprehensive DEscriptors for Structural and Statistical Analysis

CoMFA

Comparative Molecular Field Analysis

CoMSIA

Comparative Molecular Similarity Indices Analysis

CPU

Central Processor Unit

CSA

Cluster Significance Analysis

CSS

Common SubStructure

DARC

Description, Acquisition, Restitution, Computer-aided design

DF

Density Function

DISSIM

Statistical module to calculate the DISSIMilarity index

DMC

Dynamic Mapping of Consensus positions

DNA

Desoxirribo Nucleic Acid

DRAGON

Software for the calculation of molecular descriptors

FREL

Fragments Reduced to an Environment that is Limited

FM

Fragmental Methods

FO

Focus

GETAWAY

GEometry; Topology and Atom-Weights AssemblY

GMLC

Gaussian Maximum Likelihood Classification

GTM

Generative Topographic Mapping

HIV

Human Immunodeficiency Virus

HTS

High Throughput Screening

HTSS

Hierarchic Tree Substructure Search Systems

HOMO-LUMO

Highest Occupied Molecular Orbital – Lowest Unoccupied Molecular Orbital

IR

InfraRed

IUPAC

International Union of Pure and Applied Chemistry

KNN

K-Nearest Neighbors

LaSSI

Latent Semantic Structure Indexing

LDA

Linear Discriminant Analysis

MACCS

Substructure search system from CambridgeSoft Corporation

MaP

Mapping Property distributions of molecular surfaces

MDDR

MDL Drug Data Report

MEP

Molecular electrostatic Potential

MCSS

Maximal Common Sub-Structure

MQS

Molecular Quantum Similarity

NMC

Nearest Mean Classifier

PCA

Principal Component Analysis

Pm

molecular Property characteristic

QQSPR or Q2SPR

Quantum Quantitative Structure-Property Relationship

QSAR

Quantitative Structure-Activity Relationship

QSPR

Quantitative Structure-Property Relationship

QSCD

Quantized Surface Complementarity Diversity

RDF

Radial Distribution Function

RMS

Root Mean Square

S4

Substructure search software (Beilstein Institute of Organic Chemistry & Softron Ltd)

SIMCA

Soft Independent Modeling of Class Analogy

SMILES

Simplified Molecular Input Line Entry Specification

SVM

Support Vector Machines

ToPD

Total Pharmacophore Diversity

UV

UltraViolet

WLN

Wiswesser Line Notation

WHIM

Weighted Holistic Invariant Molecular

References

  1. 1.
    Richon, A.B., A History of Computational Chemistry, Network Science (1996). Available at the following URL: http://www.netsci.org/Science/Compchem/feature17a.html
  2. 2.
    Rouvray, D.H., The evolution of the concept of molecular similarity. In Johnson, M.A. and Maggiora, G.M. (Eds.) Concepts and Applications of Molecular Similarity, John Willey & Sons, New York, Inc. 1990. pp. 15–42.Google Scholar
  3. 3.
    Rouvray, D.H., Definition and role of similarity concepts in the chemical and physical sciences, J. Chem. Inf. Comp. Sci., 32 (1992) 580–586.CrossRefGoogle Scholar
  4. 4.
    Kopp, H., Ann. Chem. 41 (1842) 79. Reedited in 1954 as, Kopp, H. Ann. Annalen der Chemie und pharm, 92 (1854) 1.Google Scholar
  5. 5.
    Richardson, B.W., Rep. Brit. Assoc. Adv. Sci. 34 (1864) 120.Google Scholar
  6. 6.
    Wiener, H. Structural determination of Paraffin boiling points, J. Amer. Chem. Soc., 69 (1947) 17–20.Google Scholar
  7. 7.
    Hansch, C. and Fujita, T., r-s-p analysis – a method for the correlation of biological activity and chemical structure, J. Amer. Chem. Soc., 86 (1964) 1616–1626.Google Scholar
  8. 8.
    Marshall, G.R., Barry, C.D., Bosshard, H.E., Dammkoehler, R.A. and Dunn, D.A., in Computer-Assisted Drug Design. Olson E.C. and Christofferson R.E. (Eds.) American Chemical Society Symposium, Vol, 112, American Chemical Society, Washington D.C. 1979, 205–226.Google Scholar
  9. 9.
    Tripos, Inc., 1699 South Hanley Rd. St. Louis, Missouri, 63144, USA. Information available at the following URL: http://www.tripos.com/
  10. 10.
    Pavia, M.R., The chemical generation of molecular diversity, Network Science (1994). Available at the following URL: http://www.netsci.org/Science/Combichem/feature01.html.
  11. 11.
    DeWitt, S.H., Kiely, J.S., Stankovic, C.J., Schroeder, M.C., Reynolds Cody, D.M. and Pavia, M.R., “Diversomers”: An approach to nonpetide, nonoligomeric chemical diversity, Proc. Natl. Acad. Sci. USA, 90 (1993) 6909–6913.Google Scholar
  12. 12.
    Carhart, R.E., Smith, D.H. and Venkataraghavan, R., Atom pairs as molecular features in structure-activity studies: Definitions and applications, J. Chem. Inf. Comput. Sci., 25 (1985) 64–73.CrossRefGoogle Scholar
  13. 13.
    Willett, P., Winterman, V. and Bawden, D., Implementation of nearest neighbor searching in an online chemical structure search system, J. Chem. Inf. Comput. Sci., 26 (1986) 36–41.Google Scholar
  14. 14.
    Chabala, J., et al., Historical overview of the developing field of molecular diversity, in Gordon E. M. and Kerwin, J.F. Jr. (Eds.), Combinatorial Chemistry and Molecular Diversity in Drug Discovery, Wiley & Sons, New York, 1998, pp. 3–15.Google Scholar
  15. 15.
    Gasteiger, J. (Ed.) Handbook of Chemoinformatics. From Data to Knowledge. Volume 1 to 4. Wiley-VCH, Germany, 2003.Google Scholar
  16. 16.
    Bajorath, J. (Ed.) Chemoinformatics. concepts, methods and tools for drug discovery. Methods in Molecular Biology, vol. 275. Humana Press Inc., Totowa, NJ. 2004.Google Scholar
  17. 17.
    Johnson, A.M. and Maggiora, G.M. (Eds.) Concepts and Applications of Molecular Similarity, John Willey & Sons, New York, Inc. 1990.Google Scholar
  18. 18.
    Dean, P.M. (Ed.) Molecular Similarity in Drug Design, Chapman & Hall, New York, 1995.Google Scholar
  19. 19.
    Barbosa, F. and Horvath, D., Molecular similarity and property similarity Curr. Top. Med. Chem., 4 (2004) 589–600.Google Scholar
  20. 20.
    Perez, J.J., Managing molecular diversity, Chem. Soc. Rev., 34 (2005) 143–152.CrossRefGoogle Scholar
  21. 21.
    Bender, A. and Glen, R.C., Molecular similarity: A key technique in molecular informatics Org. Biomol. Chem., 2 (2004) 3204–3218.Google Scholar
  22. 22.
    Leach, A.R. and Gillet, V.J. (Eds.) An Introduction of Chemoinformatics, Kluwer Academic Publishers, 2003.Google Scholar
  23. 23.
    Gasteiger, J. and Engel, T. (Eds.) Chemoinformatics. A Textbook, Wiley-VCH, Germany, 2003.Google Scholar
  24. 24.
    Moos, W.H., Green, G.D. and Pavia, M.R, Chapter 33. Recent advances in the generation of molecular diversity, Annual Reports in Medicinal Chemistry, 28 (1993) 315–324.Google Scholar
  25. 25.
    Mason, J.S. and Hermsmeier, N.A., Diversity assessment, Curr. Op. Chem. Bio., 3 (1999) 342–349.Google Scholar
  26. 26.
    Warr, W.A., Commercial software systems for diversity analysis, Perspectiv. Drug Disc. Design, 7/8 (1997) 115–130.Google Scholar
  27. 27.
    Sadowski, J. and Kubinyi, H., A scoring scheme for discriminating between drugs and non drugs, J. Med. Chem., 41 (1998) 3325–3329.CrossRefGoogle Scholar
  28. 28.
    Terstappen, G.C. and Reggiani, A., In silico research in drug discovery, Trends Pharm. Sci., 22 (2001) 23–26.CrossRefGoogle Scholar
  29. 29.
    Wintner, E. and Moallemi, C.C., Quantized surface complementarity diversity (QSCD): A model based on small molecule-target complementarity, J. Med. Chem., 43 (2000) 1993–2006.CrossRefGoogle Scholar
  30. 30.
    Pearlman, R.S., Novel software tools for addressing chemical diversity, Network Science (1999). Available at the following URL: http://www.netsci.org/Science/Combichem/feature08.html
  31. 31.
    Pearlman, R.S. and Smith, K.M., Novel software tools for chemical diversity, Perspectiv. Drug Disc. Design, 9/10/11 (1998) 339–353.Google Scholar
  32. 32.
    Bures, M.G. and Martin, Y.C., Computational methods in molecular diversity and combinatorial chemistry, Curr. Opin. Chem. Biol., 2 (1998) 376–380.CrossRefGoogle Scholar
  33. 33.
    Information available at the following URL: http://pearl1.lanl.gov/periodic/mendeleev.htm
  34. 34.
    Makara G., Measuring molecular similarity and diversity: Total pharmacophore diversity, J. Med. Chem., 44 (2001) 3563–3571.CrossRefGoogle Scholar
  35. 35.
    Nikolova, N. and Jaworska, J., Approaches to measure chemical similarity – a review, QSAR Comb. Sci., 22 (2003) 1006–1026.CrossRefGoogle Scholar
  36. 36.
    Katritzky, A.R., Lobanov, V.S. and Karelson, M., CODESSA Reference Manual, Version 2.0, Gainville, 1996.Google Scholar
  37. 37.
    Information available at the following URL: http://www.disat.unimib.it/chm/QSARnews2.htm
  38. 38.
    Willett, P. (Ed.) Similarity and clustering in chemical information systems, Research Studies Press, Letchworth, Herts., U.K., 1987.Google Scholar
  39. 39.
    Pepperrell, C.A. and Willett, P., Techniques for the calculation of the three-dimensional structural similarity using inter-atomic distances, J. Comput.-Aided Mol. Design, 5 (1991) 455–474.CrossRefGoogle Scholar
  40. 40.
    Bossert, W., Pattanaik, P.K. and Xu, Y., Similarity of option and the measurement of diversity. Working paper published by the Center for Interuniversity Research in Quantitative Economics (CIREQ) under number 11-2002. Available at the following URL: http://www.sceco.umontreal.ca/publications/etext/2002-11.pdf
  41. 41.
    Petitjean, M., Geometric molecular similarity from volume-based distance minimization: Application to saxitoxin and tetrodotoxin, J. Comput. Chem., 16 (1995) 80–90.CrossRefGoogle Scholar
  42. 42.
    Petitjean, M., Three-dimensional pattern recognition from molecular distance minimization, J. Chem. Inf. Comput. Sci., 36 (1996) 1038–1049.CrossRefGoogle Scholar
  43. 43.
    Petitjean, M., From shape similarity to shape complementarity: Toward a docking theory, J. Math. Chem., 35 (2004) 147–158.CrossRefGoogle Scholar
  44. 44.
    Petitjean, M., Chiral mixtures, J. Math. Phys., 43 (2002) 4147–4157.CrossRefGoogle Scholar
  45. 45.
    Maggiora, G.M. and Shanmugasundaram, V., Molecular similarity measures. In Bajorath, J. (Ed.) Methods in Molecular Biology, vol. 275. Chemoinformatics. Concepts, Methods and Tools for Drug Discovery. Humana Press Inc., Totowa, NJ. 2004. pp.1–50.Google Scholar
  46. 46.
    Willett, P. and Winterman, V.A., Comparison of some measures for the determination of intermolecular structural similarity measures, Quant. Struct.-Act. Relat., 5 (1986) 18–25.Google Scholar
  47. 47.
    Holliday, J.D., Hu, C.Y. and Willett, P., Grouping of coefficients for the calculation of Inter-molecular similarity and dissimilarity using 2D fragment Bit-Strings, Comb. Chem. High Throughput Screening, 5 (2002) 155–166.Google Scholar
  48. 48.
    Haffri, Y., Chapter 1: Distance measures, INA Internal Report, Institut National de l'Audiovisuel (INA), France, 2003.Google Scholar
  49. 49.
    Holliday, J.D., Salim, N., Whittle, M. and Willett, P., Analysis and display of the size of chemical similarity coefficients, J. Chem. Inf. Comput. Sci., 43 (2003) 819–828.Google Scholar
  50. 50.
    Bath, P.A., Morris, C.A. and Willett, P., Effects of standardization on fragment-based measures of structural similarity, J. Chemomet., 7 (1993) 543–550.Google Scholar
  51. 51.
    Brown, R.D., Descriptors for diversity analysis, Persp. Drug Disc. Design, 7/8 (1997) 31–49.Google Scholar
  52. 52.
    Todeschini, R. and Consonni, V., Handbook of molecular descriptors, in Mannhold, R., Kubinyi, H. and Timmerman, H. (Eds.), Series of Methods and Principles of Medicinal Chemistry – vol. 11, Wiley-VCH, New York, 2000.Google Scholar
  53. 53.
    Martin, Y.C., Bures, M.G. and Brown, R.D., Validated descriptors for diversity measurements and optimization, Pharm. Pharmacol. Commun., 4 (1998) 147–152.Google Scholar
  54. 54.
    Martin, Y.C., Molecular Diversity: How we measure it? Has it lived up to its promise?, Il Farmaco 56 (2001) 137–139.Google Scholar
  55. 55.
    Willett, P., Chemoinformatics – similarity and diversity in chemical libraries, Current Opinion in Biotechnology, 11 (2000) 85–88.CrossRefGoogle Scholar
  56. 56.
    Willett, P., Barnard, J.M. and Downs, G.M., Chemical similarity searching, J. Chem. Inf. Comput. Sci., 38 (1998) 983–996.CrossRefGoogle Scholar
  57. 57.
    Gillet, V., Willett, P. and Bradshaw, J., Similarity searching using reduced graphs, J. Chem. Inf. Comput. Sci., 43 (2003) 338–345.CrossRefGoogle Scholar
  58. 58.
    Randic, M., Molecular shape profiles, J. Chem. Inf. Comput. Sci., 35 (1995) 373–382.Google Scholar
  59. 59.
    Barnard, J.M., Substructure searching methods: Old and new, J. Chem. Inf. Comput. Sci., 33 (1993) 532–538.CrossRefGoogle Scholar
  60. 60.
    Mezey, P.G., The degree of similarity of three-dimensional bodies: Application to molecular shape analysis, J. Math. Chem., 7 (1991) 39–49.Google Scholar
  61. 61.
    Todeschini, R., Lasagni, R. and Marengo, E., New molecular descriptors descriptor for 2D and 3D structures. Theory, J. Chemometrics, 8 (1994) 263–272.CrossRefGoogle Scholar
  62. 62.
    Randic, M., Molecular profiles, novel geometry-dependent molecular descriptors, New J. Chem., 19 (1995) 781–791.Google Scholar
  63. 63.
    Ghuloum, A.M., Sage, C.R., Jain, A.N., Anwar, M.G., Carleton, R.S. and Ajay, N.J., Molecular hashkeys: A novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules, J. Med. Chem., 42 (1999) 1739–1748.CrossRefGoogle Scholar
  64. 64.
    Stiefl, N. and Baumann, K., Mapping property distributions of molecular surfaces: Algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique, J. Med. Chem., 46 (2003) 1390–1407.CrossRefGoogle Scholar
  65. 65.
    Carbó, R., Leyda, L. and Arnau, M., An electron density measure of the similarity between two compounds, Int. J. Quantum Chemistry, 17 (1980) 1185–1189.Google Scholar
  66. 66.
    Kier, L.B. and Hall, L.H., An electrotopological-state index for atoms in molecules, Pharm. Res., 7 (1990) 801–807.CrossRefGoogle Scholar
  67. 67.
    Kubinyi, H. (Ed.) 3D QSAR in Drug Design: Theory, Methods and Applications, ESCOM Science Publishers B.V., Leiden, 1993.Google Scholar
  68. 68.
    Yao, J., Fan, B.T., Doucet, J.P., Panaye, A., Yuan, S. and Li, J., SIRSS-SS: A system for simulating IR/Raman spectra. 1. Substructure/subspectrum correlation, J. Chem. Inf. Comput. Sci., 41 (2001) 1046–1052.CrossRefGoogle Scholar
  69. 69.
    Panaye, A., Doucet, J.P. and Fan, B.T., Topological approach of C13-NMR spectral simulation: Application to fuzzy substructures, J. Chem. Inf. Comput. Sci., 33 (1993) 258–265.CrossRefGoogle Scholar
  70. 70.
    Davies, K. and Briant, C., Combinatorial chemistry library design using pharmacophore diversity, Network Science, (1995). Available at the following URL: http://www.netsci.org/Science/Combichem/feature05.html
  71. 71.
    Faulon, J.-L., The signature Descriptor. 1. Using extended valence sequences in QSAR and QSPR studies, J. Chem. Inf. Comput. Sci., 43 (2003) 707–720.Google Scholar
  72. 72.
    Consonni, V., Todeschini, R. and Pavan, M., Structure/response correlation and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors, J. Chem. Inf. Comput. Sci., 42 (2002) 682–692.Google Scholar
  73. 73.
    Consonni, V., Todeschini, R. and Pavan, M., Structure/response correlation and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies, J. Chem. Inf. Comput. Sci., 42 (2002) 693–705.Google Scholar
  74. 74.
    Jain, A.N., Morphological similarity: A 3D molecular similarity method correlated with protein-ligand recognition, J. Comput.-Aided Mol. Design, 14 (2000) 199–213.CrossRefGoogle Scholar
  75. 75.
    Todeschini, R. and Gramatica, P., 3D-modelling and prediction by WHIM descriptors. Part 5. Theory development and chemical meaning of WHIM descriptors, Quantum Struct.-Act. Relat., 16 (1997) 113–119.Google Scholar
  76. 76.
    Mason, J.S., Morize, I., Menard, P.R., Cheney, D.L., Hulme, C. and Labaudiniere, R.F., New 4-point pharmacophore method for molecular similarity and diversity applications: Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures, J. Med. Chem., 42 (1999) 3251–3264.CrossRefGoogle Scholar
  77. 77.
    Walters, W.P., Stahl, M.T. and Murcko, M.A. Virtual screening – an overview, Drug Discovery Today, 3 (1998) 160–178.CrossRefGoogle Scholar
  78. 78.
    Patterson, D.E., Cramer, R.D., Ferguson, A.M., Clark, R.D. and Weinberger, L.E., Neighborhood behavior: A useful concept for validation of “Molecular diversity” descriptors, J. Med. Chem., 39 (1996) 3049–3059.CrossRefGoogle Scholar
  79. 79.
    Martin, Y.C., Kofron, J.L. and Traphagen, L.M., Do structurally similar molecules have similar biological activity? J. Med. Chem., 45 (2002) 4350–4358.Google Scholar
  80. 80.
    Doucet, J.P. and Panaye, A., 3D Structural information: Form property prediction to substructure recognition with neural networks, SAR and QSAR Envirom. Res., 8 (1998) 249–272.Google Scholar
  81. 81.
    Gund, P., Andose, J.D., Rhodes, J.B. and Smith G.M., Three-dimensional molecular modeling and drug design, Science, 208 (1980) 1425–1431.Google Scholar
  82. 82.
    Doucet, J.P. and Weber, J.K. (Eds.) Computer-Aided Molecular Design. Theory and Applications, Academic Press, London, 1996.Google Scholar
  83. 83.
    Pepperrell, C.A., Taylor, R. and Willett, P., Implementation and use of an atom-mapping procedure for similarity searching in databases of three-dimensional chemical structures, Tetrahedron Computer Methodology, 3 (1990) 55–63.CrossRefGoogle Scholar
  84. 84.
    Bajorath, J., Virtual Screening in drug discovery: Methods, expectations and reality. Available at the following URL: http://www.currentdrugdiscovery.com
  85. 85.
    Turin, L. and Fumiko, Y., Structure-odor relations: A modern perspective. Available at the following URL: http://www.flexitral/research/review_final.pdf
  86. 86.
    Meylan, W.M., Howard, P.H., Boethling, R.S., Aronson, D., Printup, H. and Gouchi, S., Improved methods for estimating bioconcentration/bioaccumulation factor from Octanol/Water partition coefficient, Environ. Toxicol. Chem., 18 (1999) 664–672.CrossRefGoogle Scholar
  87. 87.
    Gorse, D., Rees, A., Kaczorek, M. and Lahana, R., Molecular diversity and its analysis, Drug Disc.Today, 4 (1999) 257–264.Google Scholar
  88. 88.
    Japertas, P., Didziapetris, R. and Petrauskas, A., Fragmental Methods in the design of new compounds. Applications of the advanced algorithm builder, QSAR, 21 (2002) 23–37.Google Scholar
  89. 89.
    Cuissart, B., Touffet, F., Crémilleux, B., Bureau, R. and Rault, S., The maximum common substructure as a molecular depiction in a supervised classification context: Experiments in quantitative structure/biodegradability relationships, J. Chem. Inf. Comput. Sci., 42 (2002) 1043–1052.CrossRefGoogle Scholar
  90. 90.
    Gasteiger, J., Empirical approaches ao the calculation of properties. In Gasteiger, J. and Engel T. (Eds.), Chemoinformatics – A Textbook, Wiley-VCH, Germany, 2003. pp. 320–337.Google Scholar
  91. 91.
    Mannhold, R., Rekker, R.F., Sonntag, C., Ter Laak, A.M., Dross, K. and Polymeropoulos, E.E., Comparative evaluation of the predictive power of calculation procedures for molecular lipophilicity, J. Pharm. Sci., 84 (1995) 1410–1419.Google Scholar
  92. 92.
    Mannhold, R. and Van de Waterbeemd, Substructure and whole molecule approaches for calculating log P, J. Comput. Aided Mol. Des., 15 (2001) 337–354.CrossRefGoogle Scholar
  93. 93.
    Norinder, U., Osterberg, T. and Artusson, P., Theoretical calculation and prediction of Caco-2 cell permeability using MolSurf parametrization and PLS statistics, Pharm. Res., 14 (1997) 1786–1791.CrossRefGoogle Scholar
  94. 94.
    Norinder, U., Osterberg, T. and Artusson, P., Theoretical calculation and prediction of intestinal absorption of drugs using MolSurf parametrization and PLS statistics, Eur. J. Pharm. Sci., 8 (1999) 49–56.CrossRefGoogle Scholar
  95. 95.
    Palm, K., Stenberg, P., Luthman, K. and Artusson, P., Polar molecular surface properties predict the intestinal absorption of drugs in humans, Pharm. Res., 14 (1997) 568–571.CrossRefGoogle Scholar
  96. 96.
    Stenberg, P., Luthman, K. and Artursson, P., Prediction of membrane permeability to peptides from calculated dynamic molecular surface properties, Pharm. Res., 16 (1999) 205–212.Google Scholar
  97. 97.
    Stenberg, P., Norinder, U., Luthman, K. and Artursson, P., Experimental and computational screening models for the prediction of intestinal drug absorption, J. Med. Chem., 44 (2001) 1927–1937.CrossRefGoogle Scholar
  98. 98.
    Bergström, A.S., Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption, Thesis book, Uppsala University, 2003.Google Scholar
  99. 99.
    Gasteiger, J., Physicochemical effects in the representation of molecular structures for drug designing. Mini Rev. Med. Chem., 3, 789–796 (2003).Google Scholar
  100. 100.
    Torrens, F., Structural, chemical topological, electrotopological and electronic structure hypotheses, Comb. Chem. High Throughput Screening, 6 (2003) 801–809.Google Scholar
  101. 101.
    Wiswesser, W.J.A. (Ed.), A Line-Formula Chemical Notation, Crowell, New Tork, 1954.Google Scholar
  102. 102.
    Smith, E.G. (Ed.) Wiswesser Line-Formula Chemical Notation Method (WLN), Mc Graw Hill, New York, 1968, pp. 77.Google Scholar
  103. 103.
    Ash, S., Cline, M.A., Homer, R.W., Hurst, T. and Smith, G.B., SLN (SYBYL line notation), J. Chem. Inf. Comput. Sci., 37 (1997) 71–79.CrossRefGoogle Scholar
  104. 104.
    Weininger, D., Weininger, A. and Weininger, J.L., SMILES (Simplified Molecular Input Line Entry System), J. Chem. Inf. Comput. Sci., 29 (1989) 97–101. For more information see the URL: http://www.daylight.com/dayhtml/smiles
  105. 105.
    Weininger, D., SMILES (Simplified Molecular Input Line Entry System), J. Chem. Inf. Comput. Sci., 28 (1988) 31–36.CrossRefGoogle Scholar
  106. 106.
    Vidal, D., Thormann, M. and Pons, M., LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities, J. Chem. Inf. Model., 45 (2005) 386–393.CrossRefGoogle Scholar
  107. 107.
    Luque Ruiz, I., Cerruelo Garcia, G. and Gomez-Nieto, M.A., Representation of the molecular topology of cyclical structures by means of cycle graphs. 2. Applications to clustering of chemical databases, J. Chem. Inf. Comp. Sci., 44 (2004) 1383–1393.Google Scholar
  108. 108.
    Cuissart, B., Touffet, F., Cremilleux, B., Bureau, R. and Rault, S., The maximum common substructure as a molecular depiction in a supervised classification context: Experiments in quantitative structure/biodegradability relationships, J. Chem. Inf. Comput. Sci., 42 (2002) 1043–1052.CrossRefGoogle Scholar
  109. 109.
    Lesk, A.M., Detection of 3D patterns of atoms in chemical structures, Comm. ACM, 22 (1979) 219–224.CrossRefGoogle Scholar
  110. 110.
    Barrow, H.G. and Burstall, R.M., Subgraph isomorphism, matching relational structures and maximal cliques, Inf. Proc. Lett., 4 (1976) 83–84.CrossRefGoogle Scholar
  111. 111.
    Ullman, J.R., An algorithm for subgraph isomorphism, J. ACM., 23 (1976) 31–42.CrossRefGoogle Scholar
  112. 112.
    Jorgensen, A.M. and Pedersen, J.T., Structural diversity of small molecule libraries, J. Chem. Inf. Comput. Sci., 41 (2001) 338–345.CrossRefGoogle Scholar
  113. 113.
    Bron, C. and Kerbosh, J., Finding all cliques of an undirected graph, Commun. ACM, 16 (1973) 575–577. Available at the following URL: http://www.nap.edu/readingroom/books/mctcc/index.html
  114. 114.
    Crandell, C.W. and Smith, D.H., Computer-assisted examination of compounds for common three-dimensional substructures, J. Chem. Inf. Comput. Sci., 23 (1983) 186–197.CrossRefGoogle Scholar
  115. 115.
    Ivanciuc, O., Taraviras, S.L. and Cabrol-Bass, D., Quasi-orthogonal basic sets of molecular graphs descriptors as a chemical diversity measure, J. Chem. Inf. Comput. Sci., 40 (2000) 126–134.Google Scholar
  116. 116.
    Randic, M. and Wilkins, C.L., Graph theoretical ordering of structures as a basis for systematic searches for regularities in molecular data, J. Phys. Chem., 83 (1979) 1525–1540.Google Scholar
  117. 117.
    Randic, M., Graph valence shells as molecular descriptors, J. Chem. Inf. Comput. Sci., 41 (2001) 627–630.Google Scholar
  118. 118.
    Takahashi, Y., Sukekawa, M. and Sasaki, S., Automatic identification of molecular similarity using reduced-graph representation of chemical structure, J. Chem. Inf. Comput. Sci., 32 (1992) 639–643.CrossRefGoogle Scholar
  119. 119.
    Gillet, V.J., Downs, G.M., Holliday, J.D., Lynch, M.F. and Dethlefsen, W., Computer Storage and retrieval of generic chemical structures in patents. 13. Reduced graph generation, J. Chem. Inf. Comput. Sci., 31 (1991) 260–270.CrossRefGoogle Scholar
  120. 120.
    Garey, M.G. and Johnson, D.S., Computers and intractability, a guide to the theory of NP-completeness, in Klee V. (Ed.), A series of books in the Mathematical Sciences, W.H. Freeman and company, New York, 1978, pp. 202–205.Google Scholar
  121. 121.
    Aires-de-Sousa, J., Gasteiger, J., Gutman, I. and Vidovic, D., Chirality codes and molecular structure, J. Chem. Inf. Comput. Sci., 44 (2004) 831–836.CrossRefGoogle Scholar
  122. 122.
    Petitjean, M., Chirality and symmetry measures: A transdisciplinary review, Entropy, 5 (2003) 271–312. Available at the following URL: http://www.mdpi.net/entropy
  123. 123.
    Fan, B.T., Panaye, A., Yao, J.H., Yuan, S.G. and Doucet, J.P., Geometric symmetry and chemical equivalence, in Hansew, P., Fowler, P. and Zheng, M. (Eds.), Discrete Mathematical Chemistry (Proceedings of the DIMACS Workshop), Rutgers University, March 23–24, Discrete Mathematical Society, USA, 2000, pp. 129–139.Google Scholar
  124. 124.
    Buda, A.B., Auf der Heyde, T. and Mislow, K., On quantifying chirality, Angew. Chem. Int. Ed. English, 31 (1992) 989–1007.CrossRefGoogle Scholar
  125. 125.
    Buda, A.B. and Mislow, K., A Hausdorff Chirality Measure, J. Am. Chem. Soc., 114 (1992) 6006–6012.CrossRefGoogle Scholar
  126. 126.
    Avnir, D., Katzenelson, O., Keinan, S., Pinsky, M., Pinto, Y., Salomon, Y. and Zabrodsky Hel-Or, H., The measurement of symmetry and chirality: Conceptual aspects, in Rouvray D.H. (Ed). Concepts in Chemistry. A Contemporary Challenge. Chap. 9, University of Georgia, Research Studies Press Ltd. Taunton, Wiley & Sons, New York, 1996, pp. 283–324.Google Scholar
  127. 127.
    Avnir, D., Zabrodsky Hel-Or, H. and Mezey, P.G., Symmetry and chirality: Continuous measures, In Raqué Schleyer P.V. (Ed.), Encyclopedia of Computational Chemistry. Vol 4, Wiley & Sons, Chichester, 1998, pp. 2890–2901.Google Scholar
  128. 128.
    Mezey, P.G., Generalized chirality and symmetry deficiency, J. Math. Chem., 23 (1998) 65–84.Google Scholar
  129. 129.
    Kuz'min, V.E., Stel'makh, I.B., Bekker, M.B. and Pozigun, D.V., Quantitative aspects of chirality. II. Analysis of dissymetry function behaviour with different changes in the structure of the model systems, J. Phys. Org. Chem., 5 (1992) 299–307.Google Scholar
  130. 130.
    Kuz'min, V.E., Stel'makh, I.B., Yudanova, I.V., Pozigun, D.V. and Bekker, M.B., Quantitative aspects of chirality. I. Method of dissymetry function, J. Phys. Org. Chem., 5 (1992) 295–298.Google Scholar
  131. 131.
    Dubois, J.E., Mercier, C. and Panaye, A., DARC topological system and computer aided design, Acta Pharm. Jugosl., 36 (1986) 135–169.Google Scholar
  132. 132.
    Dubois, J.E., Doucet, J.P., Panaye, A. and Fan, B.T., DARC site toplogical correlations: Ordered structural descriptors and property evaluation. In Devillers, J. and Balaban, T. (Eds). Topological indices and related descriptors in QSAR and QSPR, Gordon and Breach Sciences Publishers, Amsterdam, 1999, pp. 613–673.Google Scholar
  133. 133.
    Handbook of CIDS chemical search keys, Fein-Marquart Assoc. Inc. Towson, Baltimore, MD., 1973.Google Scholar
  134. 134.
    Bremser, W., Horse – A novel substructure code, Anal. Chem. Acta., 103 (1978) 355–365.CrossRefGoogle Scholar
  135. 135.
    Hull, R.D., Singh, S.B., Nachbar, R.B., Sheridan, R.P., Kearsley, S.K. and Fluder, E.M., Latent Semantic Structure Indexing (LaSSI) for defining chemical similarity, J. Med. Chem., 44 (2001) 1177–1184.Google Scholar
  136. 136.
    Xiao, Y., Qiao, Y., Zhang, J., Lin, S. and Zhang, W., A method for substructure search by atom-centered multilayer code, J. Chem. Inf. Comput. Sci., 37 (1997) 701–704.CrossRefGoogle Scholar
  137. 137.
    Bender, A., Mussa, H.Y. and Glen, R.C., Molecular Similarity searching using atoms environments, information-based feature selection and a naïve Bayesian classifier, J. Chem. Inf. Comput. Sci. 44 (2004) 170–178.Google Scholar
  138. 138.
    Xing, L. and Glen, R.C., Novel methods for the prediction of Log P, pKa and Log D, J. Chem. Inf. Comput. Sci., 42 (2002) 796–805.CrossRefGoogle Scholar
  139. 139.
    Faulon, J.L., Visco, D.P. Jr. and Pophale, R.S., The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies, J. Chem. Inf. Comput. Sci., 43 (2003) 707–720.Google Scholar
  140. 140.
    Faulon, J.L., Churchwell, C.J. and Visco, D.P. Jr., The signature Molecular Descriptor. 2. Enumerating molecules from their extended valence sequences, J. Chem. Inf. Comput. Sci., 43 (2003) 721–734.Google Scholar
  141. 141.
    Mitchell, T.M. (Ed.) Machine Learning, McGraw-Hill, New York, 1997.Google Scholar
  142. 142.
    Robinson, D.D., Barlow, T.W. and Richards, W.G., The utilization of reduced dimensional representation of molecular structure for rapid molecular similarity calculations, J. Chem. Inf. Comput. Sci., 37 (1997) 943–950.Google Scholar
  143. 143.
    Carbó, R., Leyda, L. and Arnau, M, An electron density measure of the similarity between two compounds, Int. J. Quantum Chem., 17 (1980) 1185–1189.Google Scholar
  144. 144.
    Hogking, E.E. and Richards, W.G., Molecular similarity based on electrostatic potential and electric field, Int. J. Quantum Chem. Quantum Biol. Symp., 14 (1987) 105–117.Google Scholar
  145. 145.
    Cramer, R.D., Patterson, D.E. and Bunce, J.D., Comparative molecular field analysis (CoMFA). Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc., 110 (1988) 5959–5967.CrossRefGoogle Scholar
  146. 146.
    Good, A.C., Sung-Sau, S. and Richards, W.G., Structure activity relationships from molecular similarity matrices, J. Med. Chem., 36 (1993) 433–438.Google Scholar
  147. 147.
    Pearson, K., Mathematical contributions to the theory of evolution III. Regression, heredity, and panmixia, Philos. Trans. Royal Soc., 187 (1896) 253–318.Google Scholar
  148. 148.
    Pearson, K., On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling (1900), In Karl Pearson's Early Statistical Papers, Cambridge University Press, London, 1956, pp. 339–357.Google Scholar
  149. 149.
    Klebe, G., Structural alignment of molecules, In Kubinyi H. (Ed.), 3D-QSAR in Drug Design: Theory, Methods and Applications, ESCOM, Leiden, 1993, pp 173–199.Google Scholar
  150. 150.
    Lemmen, C. and Lengauer, T., Computational methods for the structural alignment of molecules, J. Comput.-Aided Mol. Des., 14 (2000) 215–232.CrossRefGoogle Scholar
  151. 151.
    Petitjean, M., From shape similarity to shape complementarity: Toward a docking theory, J. Math. Chem., 35 (2004) 147–158.CrossRefGoogle Scholar
  152. 152.
    Grant, J.A. and Pickup, B.T., A Gaussian description of molecular shape, J. Phys. Chem., 99 (1999) 3503–3510.Google Scholar
  153. 153.
    Putta, S., Lemmen, C., Beroza, P. and Greene, J., A novel shape-feature based approach to virtual library screening, J. Chem. Inf. Comput. Sci., 42 (2002) 1230–1240.CrossRefGoogle Scholar
  154. 154.
    Hahn, M., Three-dimensional shape-based searching of conformationally flexible compounds, J. Chem. Inf. Comput. Sci., 37 (1997) 80–86.CrossRefGoogle Scholar
  155. 155.
    Putta, S., Eksterowicz, J., Lemmen, C. and Stanton, R., A novel subshape molecular descriptor, J. Chem. Inf. Comput. Sci., 43 (2003) 1623–1635.CrossRefGoogle Scholar
  156. 156.
    Semus, S.F., CoMFA: A field of dreams?, Network Science (1996). Available at the following URL: http://www.netsci.org/Science/Compchem/feature11.html
  157. 157.
    Calder, J.A., CoMFA validation of the superposition of six classes of compounds which block GABA receptors non-competitively, J. Comput.-Aided Mol. Des. 7 (1993) 45–60.Google Scholar
  158. 158.
    Horwitz, J.P., Comparative molecular field analysis of in vitro growth inhibition of L1210 and HCT-8 cells by some pyrazoloacridines, J. Med. Chem. 36 (1993) 3511–3516.Google Scholar
  159. 159.
    Klebe, G. and Abraham, U., On the prediction of binding properties of drug molecules by comparative molecular field analysis, J. Med. Chem., 36 (1993) 70–80.CrossRefGoogle Scholar
  160. 160.
    Connolly, M.L, Molecular Surfaces: A Review, Network Science (1996). Available at the following URL: http://www.netsci.org/Science/Compchem/feature14.html
  161. 161.
    Chau, P.L. and Dean, P.M., Molecular recoginition: 3D surface structure comparison by gnomic projection, J. Mol. Graph., 5 (1987) 97–100.Google Scholar
  162. 162.
    Mount, J., Ruppert, J., Welch, W. and Jain, A.N., IcePick: A flexible surface-based system for molecular diversity, J. Med. Chem., 42 (1999) 60–66.CrossRefGoogle Scholar
  163. 163.
    Rusinko III, A., Sheridan, R.P., Nilakantan, P., Haraki, K.S., Bauman, N. and Venkataraghavan, R., Using CONCORD to construct a large database of three-dimensional coordinates from connection tables, J. Chem. Inf. Comput. Sci., 29 (1989) 251–267.Google Scholar
  164. 164.
    Sadowski, J., Wagener, M. and Gasteiger, J., CORINA: Automatic generation of high-quality 3D-molecular models for application in QSAR, in Sanz, F., Giraldo, J. and Manaut F. (Eds.), QSAR and Molecular Modelling: Concepts, Computational Tools and Biological Applications. Prous Science Publishers, 1995, pp. 646–651.Google Scholar
  165. 165.
    Von Neumann, J. (Ed.) Mathematical Foundations of Quantum Mechanics, Princeton University Press, New Jersey, 1955.Google Scholar
  166. 166.
    Born, M. (Ed.) Atomic Physics, Blackie and Son Press, London, 1945.Google Scholar
  167. 167.
    Dirac, P.A.M., The Principles of Quantum Mechanics, Clarendon Press, Oxford, 1983.Google Scholar
  168. 168.
    Carbó-Dorca, R., Arnau, J. and Leyda, L., How similar is a molecule to another? An electron density measure of similarity between two molecular structures, Int. J. Quantum Chem., 17 (1980) 1185–1189.Google Scholar
  169. 169.
    Carbó-Dorca, R., Martin, M. and Pons, V., Applications of quantum mechanical parameters in quantitative structure-activity relationships, Afinidad, 34 (1977) 348–353.Google Scholar
  170. 170.
    Eyring, H., Walter, J. and Kimball, G.E. (Eds.) Quantum Chemistry, Wiley & Sons, New York, 1944.Google Scholar
  171. 171.
    Carbó-Dorca, R., Calabuig, B., Vera, L. and Besalú, E., Molecular quantum similarity: Theoretical framework, ordering principles and visualization techniques, Adv. Quantum Chem., 25 (1994) 253–313.Google Scholar
  172. 172.
    Carbó-Dorca, R., Robert, D., Amat, L.I., Gironés, X. and Besalú, E., Molecular quantum similarity in QSAR and drug design, Lecture Notes in Chemistry, 2000, 73, Springer-Verlag, Berlin.Google Scholar
  173. 173.
    Carbó-Dorca, R., Quantum quantitative structure-activity relationships (QQSPR): A comprehensive discuccion based on inward matrix products, employed as a tool to find approximate solutions of strictly positive lineat systems and providing QSAR-quantum similarity measures connection, (Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2000)), Barcelona, Spain, September 11–14, CDROM, ISBN 84-89925-70-4, CIMNE, Barcelona, 2000.Google Scholar
  174. 174.
    Janesko, B.G. and Yaron, D., Using molecular similarity to construct accurate semiempirical electronic structure theories, J. Chem. Phys., 121 (2004) 5635–5645.CrossRefGoogle Scholar
  175. 175.
    Xian, B., Li, T., Sun, G. and Cao, T., The combination of principal component analysis, genetic algorithm and tabu search in 3D molecular similarity, J. Molec. Struct. (Theochem) 674 (2004) 87–97.Google Scholar
  176. 176.
    Davies, E.K. and Briant C., Combinatorial chemistry library design using pharmacophore diversity, Network Science. 1995. Available at the following URL: http://www.netsci.org/Science/Combichem/feature05.html
  177. 177.
    The Unity software packages are available from Tripos Inc at URL: http://www.tripos.com/
  178. 178.
    Godden, J.W., Furr, J.R., Xue, L., Stahura, F.L. and Bajorath, J., Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformated chemical descriptor spaces with variable dimensionality, J. Chem. Inf. Comput. Sci., 44 (2004) 21–29.CrossRefGoogle Scholar
  179. 179.
  180. 180.
    Arnold, J.R., Burdick, K.W., Pegg, S.C., Toba, S., Lamb, M.L. and Kuntz, I.D., SitePrint: Three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification and drug design. J. Chem. Inf. Comput. Sci., 44 (2004) 2190–2198.CrossRefGoogle Scholar
  181. 181.
    Horvart, D. and Mao, B., Neighborhood behavior. Fuzzy molecular descriptors and their influence on the relationships between structural similarity and property similarity, QSAR Comb. Sci., 22 (2003) 498–509.Google Scholar
  182. 182.
    Jenkins, J.L., Glick M. and Davies, J.W., A 3D similarity method for scaffold hopping from know drugs or natural ligands to new chemotypes, J. Med. Chem., 47 (2004) 6144–6159.CrossRefGoogle Scholar
  183. 183.
    Renner, S. and Schneider, G., Fuzzy pharmacophore models from molecular alignements for correlation-vector-based virtual screening, J. Med. Chem., 47 (2004) 4653–4664.CrossRefGoogle Scholar
  184. 184.
    Rhodes, N. and Willett, P., CLIP: Similarity searching of 3D databases using clique detection, J. Chem. Inf. Comput. Sci., 43 (2003) 443–448.CrossRefGoogle Scholar
  185. 185.
    Todeschini, R. and Consonni, V., Dragon, release 1.12 for Windows, Milano, Italy, 2001. For more information see the URL: http://www.disat.unimib.it/chm/Dragon.htm
  186. 186.
    Selwood, D.L., Livingstone, D.J., Comley, J.C.W., O'Dowd, A.B., Hudson, A.T., Jackson, P., Jandu, K.S., Rose, V.S. and Stables J.N., Structure-activity relationships of antifilarial antimycin analogues, a multivariate pattern recognition study, J. Med. Chem., 33 (1990) 136–142.CrossRefGoogle Scholar
  187. 187.
    Zheng, W. and Tropsha, A., Novel variable selection quantitative structure-property relationship approach based on the k-nearest neighbour principle, J. Chem. Inf. Comput. Sci., 40 (2000) 185–194.CrossRefGoogle Scholar
  188. 188.
    Sutter, J.M., Dixon, S.L. and Jurs, P.C., Automated descriptor selection for quantitative structure-activity relationships using generalised simulated annealing, J. Chem. Inf. Comput. Sci., 35 (1995) 77–84.CrossRefGoogle Scholar
  189. 189.
    Kubinyi, H., Variable selection in QSAR studies. I. An evolutionary algorithm, QSAR, 13 (1994) 285–294.Google Scholar
  190. 190.
    Luke, B.T., Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comput. Sci., 34 (1994) 1279–1287.CrossRefGoogle Scholar
  191. 191.
    Waller, C.L. and Bradley, M.P., Development and validation of a novel variable selection technique with application to multidimensional quantitative structure-activity relationship studies, J. Chem. Inf. Comput. Sci., 39 (1999) 345–355.CrossRefGoogle Scholar
  192. 192.
    Hasegawa, K. and Funatsu, K., Genetic algorithm strategy for variable selection in QSAR studies. GAPLS and D-optimal design for predictive QSAR studies, J. Mol. Struct. (Theochem), 425 (1998) 255–262.CrossRefGoogle Scholar
  193. 193.
    Jouan-Rimbaud, D., Massart, D.L. and De Noord, O.E., Random correlations in variable selection for multivariate calibration with a genetic algorithm, Chemom. Intell. Lab. Syst., 35 (1996) 213–220.CrossRefGoogle Scholar
  194. 194.
    Rogers, D.R. and Hopfinger, A.J., Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comput. Sci., 34 (1994) 854–866.Google Scholar
  195. 195.
    Nath, R., Rajagopalan, B. and Ryker, R., Determining the saliency of input variables in neural networks classifiers, Comput. Ops. Res., 24 (1997) 767–773.Google Scholar
  196. 196.
    Koivalishyn, V., Tetko, V.I., Luik, A.I., Kholodovych, V.V., Villa, A.E.P. and Livingstone, D.J., Neural networks studies. 3. Variable selection in the cascade-correlation learning architecture, J. Chem. Inf. Comput. Sci., 38 (1998) 651–659.Google Scholar
  197. 197.
    Todeschini, R., Galvagni, D., Vilchez, J.L., Del Olmo, M. and Navas, N., Kohonen artificial neural networks as a tool for wawelength selection in multicomponent spectrofluorimetric PLS modeling: application to phenol, o-cresol, m-cresol and p-cresol mixtures, Trends Anal. Chem., 18 (1999) 93–98.CrossRefGoogle Scholar
  198. 198.
    Burden, F.D., Ford, M.G., Whitley, D.C. and Winkler, D.A., Use of automatic relevance determination in QSAR studies using Bayesian neural networks, J. Chem. Inf. Comput. Sci., 40 (2000) 1423–1430.CrossRefGoogle Scholar
  199. 199.
    Agrafiotis, D.K. and Cedeno, W., Feature selection for structure-activity correlation using binary particle swarms., J. Med. Chem., 45 (2002) 1098–1107.Google Scholar
  200. 200.
    Izrailev, S. and Agrafiotis, D.K., A novel method for building regression tree models for QSAR based on artificial ant colony systems, J. Chem. Inf. Comput. Sci., 41 (2001) 176–180.Google Scholar
  201. 201.
    Izrailev, S. and Agrafiotis, D.K., Variable selection for QSAR by artificial ant colony systems, SAR QSAR Environ. Res., 13 (2002) 417–423.Google Scholar
  202. 202.
    Tetko, I.V., Villa, A.E. and Livingstone, D.J., Neural network studies. 2. Variable selection, J. Chem. Inf. Comput. Sci., 36 (1996) 794–803.CrossRefGoogle Scholar
  203. 203.
    Böcker, A., Schneider, G. and Teckentrup, A., Status of HTS data mining approaches, QSAR Comb. Sci., 23 (2004) 207–213.Google Scholar
  204. 204.
    Bayada, D.M., Hamersma, H. and Van Geerestein, V.J., Molecular diversity and representativity in chemical databases, J. Chem. Inf. Comput. Sci., 39 (1999) 1–10.CrossRefGoogle Scholar
  205. 205.
    Piclin, N., Screening virtuel de grandes bases de données: validation de méthodes et application en chimie pharmaceutique et en toxicité, Thesis book, Université d'Orleans, 2002.Google Scholar
  206. 206.
    Haykin, S., Neural Networks: A Comprenhensive Foundation, Prentice-Hall, 1999.Google Scholar
  207. 207.
    Czerminski, R., Yasri, A. and Hartsough, D., Use of support vector machine in pattern classification: Application to QSAR studies, Quant. Struct.-Act. Relat., 20 (2001) 345–351.CrossRefGoogle Scholar
  208. 208.
    Li, Q., Yao, X., Chen, X., Liu, M., Zhang, R., Zhan, X. and Hu, Z., Application of artificial neural networks for the simultaneous determination of a mixture of fluorescent dyes by synchronous fluorescence, The Analyst, 125 (2000) 2049–2053.Google Scholar
  209. 209.
    Agrafiotis, D.K., Cedeño, W. and Lobanov, V.S., On the use of neural networks in QSAR and QSPR, J. Chem. Inf. Comput. Sci., 42 (2002) 903–911.Google Scholar
  210. 210.
    Murcia-Soler, M., Pérez-Giménez, F., Garcia-March, F.J., Salabert-Salvador, M.T., Dias-Villanueva, W. and Castro-Bleda, M.J., Drugs and nondrugs: An effective discrimination with topological methods and artificial neural networks, J. Chem. Inf. Comput. Sci., 43 (2003) 1688–1702.CrossRefGoogle Scholar
  211. 211.
    Ma, Q.L., Yan, A.X., Hu, Z.D., Li, Z.X. and Fan, B.T., Principal component analysis and artificial neural networks applied to the classification of Chinese pottery of neolithic age, Analy. Chim. Acta., 406 (2000) 247–256.Google Scholar
  212. 212.
    Gasteiger, J., Teckentrup, A., Terfloth, L. and Spycher, S., Neural networks as data mining tools in drug design, J. Phys. Org. Chem., 16 (2003) 232–245.CrossRefGoogle Scholar
  213. 213.
    Terfloth, L. and Gasteiger, J., Self-organizing neural networks in drug design, Screening – Trends in Drug Discovery, 2 (2001) 49–51.Google Scholar
  214. 214.
    Zupan, J. and Gasteiger, J., Neural Networks in Chemistry and Drug Design, Second Edition. Wiley-VCH Publishers, Weinheim, 1999.Google Scholar
  215. 215.
    Zupan, J. and Gasteiger, J. (Eds.) Neural Networks for Chemists: An Introduction, VCH-Verlag, Weinheim, 1993.Google Scholar
  216. 216.
    Dreiseitl, S. and Ohno-Machado, L., Logistic regression and artificial neural network classification models: A methodology review, J. Biomedical Inform., 35 (2002) 352–359.Google Scholar
  217. 217.
    Manallack, D.T. and Livingstone, D.J., Neural Networks in drug discovery: Have they lived up their promise?, Eur. J. Med. Chem., 34 (1999) 195–208.CrossRefGoogle Scholar
  218. 218.
    Niculescu, S.P., Artificial neural network and genetic algorithm in QSAR, J. Mol. Struc. (Theochem.), 622 (2003) 71–83.Google Scholar
  219. 219.
    Smits, J.R.M., Melssen, W.J., Buydens, L.M.C. and Kateman, G., Using artificial neural networks for solving chemical problems. Part I. Multi-layer feed-forward networks, Chemom. Intel. Lab. Syst., 23 (1994) 165–189.Google Scholar
  220. 220.
    Melssen, W.J., Smits, J.R.M., Buydens, L.M.C. and Kateman, G., Using artificial neural networks for solving chemical problems. Part II. Kohonen Self-organising feature maps and Hopfield networks, Chemom. Intel. Lab. Syst., 23 (1994) 267–291.Google Scholar
  221. 221.
    Richards, J. and Jia, X., Remote Sensing Digital Image Analysis – An introduction, Springer, Third Ed., New York, 2000.Google Scholar
  222. 222.
    Ho, P., Silva, M.C. and Hogg, T.A., Multiple imputation and maximum likelihood principal component analysis of incomplete multivariate data from a study of the ageing of port, Chemom. Intell. Lab. Syst. 55 (2001) 1–11.Google Scholar
  223. 223.
    Andrews, T.D. and Wentzell, P., Applications of maximum likelihood principal component analysis: Incomplete data sets and calibration transfer, Analytica Chimica Acta, 350 (1997) 341–352.CrossRefGoogle Scholar
  224. 224.
    Pereira, J.L., Pais, A.C. and Redinha, J.S. Maximum likelihood estimation with nonlinear regression in polarographic and potentiometric studies, Analytica Chimica Acta, 433 (2001) 135–143.CrossRefGoogle Scholar
  225. 225.
    Verdonck, F., Jaworskab, J., Thasa, O. and Vanrolleghema, P.A., Determining environmental standards using bootstrapping, bayesian and maximum likelihood techniques: A comparative study, Analytica Chimica Acta, 446 (2001) 427–436.CrossRefGoogle Scholar
  226. 226.
    Kuttatharmmakul, S., Smeyers-Verbeke, J. and Noack, D.L., The mean and standard deviation of data, some of which are below the detection limit: An introduction to maximum likelihood estimation, TrAC, Trends in Analytical Chemistry, 19 (2000) 215–222.Google Scholar
  227. 227.
    Wentzell, P. and Lohnes, M.T., Maximum likelihood principal component analysis with correlated measurement errors: Theoretical and practical considerations, Chemometrics and Intelligent Laboratory Systems, 45 (1999).Google Scholar
  228. 228.
    Cortes, C. and Vapnik, V., Support-vector networks, Machine Learning, 20 (1995) 273–297.Google Scholar
  229. 229.
    Vapnik, V., The nature of Statistical Learning Theory, Springer, Berlin, 1995.Google Scholar
  230. 230.
    Burbidge, R., Trotter, M., Buxton, B. and Holden, S., Drug design by machine learning: Support vector machines for pharmaceutical data analysis, Comput. Chem., 26 (2001) 5–14.CrossRefGoogle Scholar
  231. 231.
    Warmuth, M.K., Liao, J., Ratsch, G., Mathieson, M., Putta, S. and Lemmen, C., Active learning with support vector machines in the drug discovery process, J. Chem. Inf. Comput. Sci., 43 (2003) 667–673.CrossRefGoogle Scholar
  232. 232.
    Wilton, D., Willett, P., Lawson, K. and Mullier, G., Comparison of ranking methods for virtual screening in lead discovery programs, J. Chem. Inf. Comput. Sci., 43 (2003) 469–474.CrossRefGoogle Scholar
  233. 233.
    Zernov, V.V., Balakin, K.V., Ivanschzenko, A.A., Savchuk, N.P. and Pletnev, I.V., Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness and enzyme inhibition predictions, J. Chem. Inf. Comput. Sci., 43 (2003) 2048–2056.CrossRefGoogle Scholar
  234. 234.
    Norinder, U., Support vector machine models in drug design: Applications to drug transport processes and QSAR using simple optimization and variable selection, Neurocomputing, 55 (2003) 337–346.CrossRefGoogle Scholar
  235. 235.
    Byvatov, E., Fechner, U., Sadowski, J. and Schneider, G., Comparison of support vector machine and artificial neural networks systems for drug/nondrug classification, J. Chem. Inf. Comput. Sci., 43 (2003) 1882–1889.CrossRefGoogle Scholar
  236. 236.
    Teckentrup, A., Briem H. and Gasteiger, J., Mining High-Throughput screening data of combinatorial libraries: Development of a filter to distinguish hits from non hits, J. Chem. Inf. Comput. Sci., 44 (2004) 626–634.CrossRefGoogle Scholar
  237. 237.
    Liu, H.X., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D. and Fan, B.T., QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-Diarylimidazoles based on support vector machines, Journal of Computer-Aided Molecular Design, 18 (2004) 389–399.Google Scholar
  238. 238.
    Liu, H.X., Zhang, R.S., Luan, F., Yao, X.J., Liu, M.C., Hu, Z.D. and Fan, B.T., Diagnosing breast cancer based on support vector machines, J. Chem. Inf. Comput. Sci., 43 (2003) 900–907.Google Scholar
  239. 239.
    Liu, H.X., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D. and Fan, B.T., QSAR study of ethyl 2-[3-methyl-2,5-dioxo(3-pyrrolinyl)amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate: An inhibitor of AP-1 and NF-kB mediated gene expression based on support vector machines, J. Chem. Inf. Comput. Sci., 43 (2003) 1288–1296.Google Scholar
  240. 240.
    Yao, X., Zhang, R.S., Chen, H., Doucet, J.P., Panaye, A., Fan, B.T., Liu, M. and Hu, Z., Comparative classification Study of Toxicity Mechanisms Using Support Vector Machines and Radial Basis Function Neural Networks, Anal. Chim. Acta, in press.Google Scholar
  241. 241.
    Xue, C.X., Zhang, R.S., Liu, H.X., Yao, X.J., Liu, M.C., Hu, Z.D. and Fan, B.T., QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine, J. Chem. Inf. Comput. Sci., 44 (2004) 1693–1700.Google Scholar
  242. 242.
    Zhao, C.Y., Zhang, R.S., Liu, H.X., Xue, C.X., Zhao, S.G., Zhou, X.F., Liu, M.C. and Fan, B.T., Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines, J. Chem. Inf. Comput. Sci., 44 (2004) 2040–2046.CrossRefGoogle Scholar
  243. 243.
    Fix, E. and Hodges, J., Discriminatory analysis. Nonparametric discrimination: Consistency properties, Technical Report 4, USAF School of Aviation Medicine, Texas, 1951.Google Scholar
  244. 244.
    Beckonert, O., Bollard, M.E., Ebbels, T., Keun, H., Antti, H., Holmes, E., Lindon, J.C. and Nicholson, J.K., NMR-based metabonomic toxicity classification: Hierarchical cluster analysis and k-nearest-neighbour approaches, Analytica Chimica Acta, 490 (2003) 3–15.CrossRefGoogle Scholar
  245. 245.
    O'Farrell, M., Lewis, E., Flanagan, C., Lyons, W. and Jackman, N., Comparison of k-NN and neural network methods in the classification of spectral data from an optical fibre-based sensor system used for quality control in the food industry, Sensors and Actuators B: Chemical, In Press, Corrected Proof, 2005.Google Scholar
  246. 246.
    Amendolia, S.R., Cossu, G., Ganadu, M.L., Golosio, B., Masala, G.L. and Mura, G.M., A comparative study of k-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening, Chemometrics and Intelligent Laboratory Systems, 69 (2003) 13–20.CrossRefGoogle Scholar
  247. 247.
    Jarvis, R.A. and Patrick, E.A., The Jarvis-Patrick algorithm – clustering using a similarity measure based on nearest neighbors, IEEE Trans. Comput. 22 (1973) 1025–1034.Google Scholar
  248. 248.
    Ward, J.H., Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc., 58 (1963) 236–244.Google Scholar
  249. 249.
    Barnard, J.M. and Downs, G.M., Clustering on chemical structures on the basis of two-dimensional similarity measures, J. Chem. Inf. Comput. Sci., 32 (1992) 664–649.CrossRefGoogle Scholar
  250. 250.
    Matter, H., Selecting optimally diverse compounds from structure databases: A validation study of two-dimensional and three-dimensional molecular descriptors, J. Med. Chem., 40 (1997) 1219–1229.CrossRefGoogle Scholar
  251. 251.
    Gooden, J.W. and Bajorath, J., Cel-based partitioning, In Bajorath, J. (Ed.), Methods in Molecular Biology, vol. 275. Chemoinformatics. Concepts, Methods and Tools for Drug Discovery. Humana Press Inc., Totowa, NJ. 2004, pp. 291–300.Google Scholar
  252. 252.
    Kohonen, T., Self-Organization and Associative Memory, Springer-Verlag, 1989.Google Scholar
  253. 253.
    Bishop, C.M. and Tipping, M.E., Latent variable models and data visualization, In Kay, J.W. and Titterington, D.M. (Eds.), Statistics and Neural Networks, Oxford University Press, London, 1999, pp. 141–164.Google Scholar
  254. 254.
    Scholkopf, B., Smola, A.J. and Muller, K.R., Kernel principal component analysis, Available at the following URL: http://mlg.anu.edu.au/~smola/papers/SchSmoMul99.pdf
  255. 255.
    Schölkopf, B., Burges, C.J.C. and Smola, A.J. (Eds.) Advances in Kernel Methods, MIT Press, Cambridge, 1999, pp. 327–352.Google Scholar
  256. 256.
    Taboureau, O., BioInformatique et drug design: contribution à l'exploitation de grandes bases de données chimiques, Thesis book, Université d'Orleans, 2001.Google Scholar
  257. 257.
    Spycher, S., Nendza, M. and Gasteiger, J., Comparison of different classification methods applied to a mode of toxic action data set, QSAR Comb. Sci., 23 (2004) 779–791.CrossRefGoogle Scholar
  258. 258.
    Feher, M. and Schmidt, J.M., Property Distributions: differences between drugs, natural products and molecules from combinatorial chemistry, J. Chem. Inf. Comput. Sci., 43 (2003) 218–227.Google Scholar
  259. 259.
    Lanctot, J.K., Putta, S., Lemmen, C. and Greene, J., Using ensembles to classify compounds for drug discovery, J. Chem. Inf. Comput. Sci., 43 (2003) 2163–2169.CrossRefGoogle Scholar
  260. 260.
    Clark, R.D., OptiSim: An extended dissimilarity selection method for finding diverse representative subsets, J. Chem. Inf. Comput. Sci., 37 (1997) 1181–1188.Google Scholar
  261. 261.
    Lobanov, V.S. and Agrafiotis, D.K., Stochastic similarity selections from large combinatorial libraries, J. Chem. Inf. Comp. Sci., 40 (2000) 460–470.CrossRefGoogle Scholar
  262. 262.
    Lin, S.-K., Molecular diversity assessment: Logarithmic relations of information and species diversity and logarithmic relations of entropy and indistinguishability after rejection of Gibbs paradox of entropy of mixing, Molecules, 1 (1996) 57–67.Google Scholar
  263. 263.
    Agrafiotis, D.K., On the use of information theory for assessing molecular diversity, J. Chem. Inf. Comp. Sci., 37 (1997) 576–580.Google Scholar
  264. 264.
    Dudoit, S., Fridlyand, J. and Speed, T.P., Comparison of discrimination methods for the classification of tumors using gene expression data, JASA, 97 (2002) No. 457.Google Scholar
  265. 265.
    Lajiness M.S., Dissimilarity-based compound selection techniques, Persp. Drug Discuss. Design, 7/8 (1997) 65–84.Google Scholar
  266. 266.
    Mason, J.S. and Picket S.D., Partition-based selection, Perspect. Drug Disc. Design, 7/8 (1997) 85–114.Google Scholar
  267. 267.
    Godden, J.W., Xue, L., Kitchen, D.B., Stahura, F.L., Schermerhorn, E.J. and Bajorath, J., Median partitioning: A novel method for the selection of representative subsets from large compound pools, J. Chem. Inf. Comput. Sci. 42 (2002) 885–893.Google Scholar
  268. 268.
    Godden, J.W., Xue, L. and Bajorath, J., Classification of biologically active compounds by median partitioning, J. Chem. Inf. Comput. Sci., 42 (2002) 1263–1269.Google Scholar
  269. 269.
    Shanmugasundaran, V., Maggiora, G.M. and Lajiness, M.S., Hit-directed nearest-neighbor searching, J. Med. Chem., 48 (2005) 240–248.Google Scholar
  270. 270.
    Cramer, R.D., Jilek, R.J., Guessregen, S., Clark, S.J., Wendt, B. and Clark, R.D. “Lead hoping”. Validation of topomer similarity as a superior predictor of similar biological activities, J. Med. Chem., 47 (2004) 6777–6791.CrossRefGoogle Scholar
  271. 271.
    Bernard, P., Modélisation et diversité moléculaires des inhibiteurs de l'acétylcholinestérase, Thesis book, Université d'Orleans, 1998.Google Scholar
  272. 272.
    Brown, R.D. and Martin, Y.C., The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding, J. Chem. Inf. Comput. Sci., 37 (1997) 1–9.CrossRefGoogle Scholar
  273. 273.
    Trepalin, S.V., Gerasimenko, V.A., Kozyukov, A.V., Savchuk, N.P. and Ivanschenko, A.A., New diversity calculations algorithms used for compound selection, J. Chem. Inf. Comput. Sci., 42 (2002) 249–258.CrossRefGoogle Scholar
  274. 274.
    Boon, G., Langenaeker, W., De Proft, F., De Winter, H., Tollenaere, J.P. and Geerlings, P., Systematic study of the quality of various quantum similarity descriptors. Use of the autocorrelation function and principal component analysis, J. Phys. Chem. A., 105 (2001) 8805–8814.CrossRefGoogle Scholar
  275. 275.
    White, M. and Willett, P., Evaluation of similarity measures for searching the dictionary of natural products database, J. Chem. Inf. Comput. Sci., 43 (2003) 449–457.Google Scholar
  276. 276.
    Dixon, S.L. and Koehler, R.T., The hidden component of size in two-dimensional fragment descriptors: Side effects on sampling in bioactive libraries, J. Med. Chem., 42 (1999) 2887–2900.CrossRefGoogle Scholar
  277. 277.
    Fligner, M.A., Verducci, J.S. and Blower, P.E., A modification of the Jaccard-Tanimoto similarity index for diverse selection of chemical compounds using binary strings, Technometrics, 44 (2002) 110–119.CrossRefGoogle Scholar
  278. 278.
    Salim, N., Analysis and comparison of molecular similarity measures, Thesis book, University of Sheffield, 2002.Google Scholar
  279. 279.
    Chen, X. and Reynolds, C.H., Performance of similarity measures in 2D fragment-based similarity searching: Comparison of structural descriptors and similarity coefficients, J. Chem. Inf. Comput. Sci., 42 (2002) 1407–1414.Google Scholar
  280. 280.
    Xue, L., Godden, J.W., Stahura, F.L. and Bajorath, J., Design and evaluation of a molecular fingerprint involving the transformation of property descriptor values into a binary classification scheme, J. Chem. Inf. Comput. Sci., 43 (2003) 1151–1157.Google Scholar
  281. 281.
    Whitley, D.C., Ford, M.G. and Livingstone, D.J., Unsupervised forward selection: A method for eliminating redundant variables, J. Chem. Inf. Comput. Sci., 40 (2000) 1160–1168.CrossRefGoogle Scholar
  282. 282.
    Rarey, M. and Dixon, J.S., Feature trees: A new molecular similarity measure based on tree matching, J. Comput.-Aided Molec. Design, 12 (1998) 471–490.Google Scholar
  283. 283.
    Dixon, S.L. and Villar, H.O., Bioactive diversity and screening library selection via affinity fingerprinting, J. Chem. Inf. Comput. Sci., 38 (1998) 1192–1203.CrossRefGoogle Scholar
  284. 284.
    Randic, M. and Basak, S., A new descriptor for structure-property and structure-activity correlations, J. Chem. Inf. Comput. Sci., 41 (2001) 650–656.Google Scholar
  285. 285.
    Ehresmann, B., de Groot, M.J., Alex, A. and Clark, T., New molecular descriptors based on local properties at the molecular surface and a boiling-point model derived from them, J. Chem. Inf. Comput. Sci., 44 (2004) 658–668.CrossRefGoogle Scholar
  286. 286.
    Whittle, M., Gillet, V. and Willett, P., Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: A comparison of similarity coefficients, J. Chem. Inf. Comput. Sci., 44 (2004) 1840–1848.CrossRefGoogle Scholar
  287. 287.
    Godden, J.W., Stahura, F.L. and Bajorath, J., Variability of molecular descriptors in compound databases revealed by shannon entropy calculations, J. Chem. Inf. Comput. Sci., 40 (2000) 796–800.Google Scholar
  288. 288.
    Xue, L., Godden, J.W., Stahura, F.L. and Bajorath, J., Similarity searching profiles as a diagnostic tool for the analysis of virtual screening calculations, J. Chem. Inf. Comput. Sci., 44 (2004).Google Scholar
  289. 289.
    Sun, H., A universal molecular descriptor system for prediction of logP, logS, logBB and absorption, J. Chem. Inf. Comput. Sci., 44 (2004) 748–757.Google Scholar
  290. 290.
    Feng, J., Lurati, L. and Ouyang, H., Predictive toxicology: Benchmarking molecular descriptors and statistical methods, J. Chem. Inf. Comput. Sci., 43 (2003) 1463–1470.Google Scholar
  291. 291.
    Schuffenhauer, A., Gillet, V.J. and Willett, P., Similarity searching in files of three-dimensional chemical structures: Analysis of the BIOSTER database using two-dimensional fingerprints and molecular field descriptors, J. Chem. Inf. Comput. Sci., 40 (2000) 295–307. 1275–1281.Google Scholar
  292. 292.
    Hicks, M.G. and Jochum, C., Substructure search systems. 1. Performance comparison of the MACCS, DARC, HTSS, CAS Registry MVSSS and S4 substructure search systems, J. Chem. Inf. Comput. Sci., 30 (1990) 191–199.Google Scholar
  293. 293.
    Good, A.C. and Richards, W.G., Explicit calculation of 3D molecular Similarity, Perspectiv. Drug Disc. Design, 9/10/11 (1998) 321–338.Google Scholar
  294. 294.
    Flower, D.R., DISSIM: A program for the analysis of chemical diversity, J. Molec. Graph. Mod., 16 (1998) 239–253.Google Scholar
  295. 295.
    Brown, R.D. and Martin, Y.C., Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compounds selection, J. Chem. Inf. Comput. Sci., 36 (1996) 572–584.CrossRefGoogle Scholar
  296. 296.
    Moos, W.H., Combinatorial chemistry: A “Molecular diversity space” Odyssey approaches 2001, Pharmaceutical News, 3 (1996) 23–26.Google Scholar
  297. 297.
    Information available at the following URL: http://www.5z.com/divinfo/reviews.html
  298. 298.
    Blaney, J.M. and Martin, E.J., Computational approaches for combinatorial library design and molecular diversity analysis, Curr. Opin. Chem. Biol., 1 (1997) 54–59.CrossRefGoogle Scholar
  299. 299.
    Pavia, M.R., Sawyer, T.K. and Moos, W.H., The generation of molecular diversity, BioMed. Chem. Lett., 3 (1993) 387–396.Google Scholar
  300. 300.
    Stu, Borman, The many faces of combinatorial chemistry, Chem. Engin. News, 81 (2003) 45–56.Google Scholar
  301. 301.
    Blaney, J.M. and Martin, E.J., Computational approaches for combinatorial library design and molecular diversity analysis, Curr. Op. Chem. Bio., 1 (1997) 54–59.Google Scholar
  302. 302.
    Willett, P., Using computational tools to analyze molecular diversity, in DeWitt, H. and Czarnik, A.W. (Eds.), Combinatorial Chemistry; A Short Course, American Chemical Society Books, Washington DC, 1997.Google Scholar
  303. 303.
    Martin, Y.C., Brown, R.D. and Bures, M.G., Quantifying diversity, in Kerwin, J.F. and Gordon, E.M. (Eds.), Combinatorial Chemistry and Molecular Diversity, Wiley & Sons, New York, 1998.Google Scholar
  304. 304.
    Weber, L., High-diversity combinatorial libraries, Curr. Op. Chem. Bio., 4 (2000) 295–302.Google Scholar
  305. 305.
    Information available at the following URL: http://www.5z.com/divinfo/links/
  306. 306.
    Information available at the following URL: http://www.combichemlab.com/
  307. 307.
    Gute, B.D. and Basak, S.C., Molecular similarity-based estimation of properties: A comparison of three structure spaces, Mol. Graph. Mod., 20 (2001) 95–109.Google Scholar
  308. 308.
    Taraviras, S., Evaluation de la diversité moléculaire des bases de données de molécules à l'intérêt pharmaceutique, en utilisant la théorie des graphes chimiques, Livre de thèse, Université de Nice-Sophia Antipolis, 2000.Google Scholar
  309. 309.
    Martin, E. and Wong, A., Sensitivity analysis and other improvements to tailored combinatorial library design, J. Chem. Inf. Comput. Sci., 40 (2000) 215–220.CrossRefGoogle Scholar
  310. 310.
    MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA 94577, USA. For more information see the URL: http://www.mdli.com
  311. 311.
    Daylight Chemical Information Systems, Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA. For more information see the URL: http://www.daylight.com
  312. 312.
    CambridgeSoft Corporation, 100 Cambridge Park Drive, Cambridge, MA 02140, USA. For more information see the URL: http://www.camsoft.com
  313. 313.
    Oxford Molecular Ltd. Medawar Centre, Oxford Science Park, Sandford-on-Thames, Oxford, OX4 4GA, UK. For more information see the URL: http://www.oxmol-.co.uk/
  314. 314.
    Synopsys Scientific Systems Ltd. 175 Woodhouse Lane, Leeds, LS2 3AR, UK. For more information see the URL: http://www.synopsys.co.uk/
  315. 315.
    Agrafiotis, D.K., Lobanov, V.S. and Salemme, F.R., Combinatorial informatics in the post-genomics era, Nature Reviews Drug Discovery, 1 (2002) 337–346.CrossRefGoogle Scholar
  316. 316.
    Schuffenhauer, A., Popov, M., Schopfer, U., Acklin, P., Stanek, J. and Jacoby, E., Molecular mangement strategies for building and enhancement of diver and focused lead discovery compound screenin collections. Comb. Chem. & HTS, 7 (2004) 771–781.Google Scholar
  317. 317.
    Miller, J.L., Bradley, E.K. and Teig, S.L., Luddite: An information-theoretic library design tool, J. Chem. Inf. Comput. Sci., 43 (2003) 47–54.Google Scholar
  318. 318.
    Young, S.S., Wang, M. and Gu, F., Design of diverse and focused combinatorial libraries using an alternative algorithm, J. Chem. Inf. Comput. Sci., 43 (2003) 1916–1921.CrossRefGoogle Scholar
  319. 319.
    Darvas, F., Dorman G. and Papp A., Diversity measures for enhacing ADME admissibility of combinatorial libraires, J. Chem. Inf. Comput. Sci., 40 (2000) 314–322.CrossRefGoogle Scholar
  320. 320.
    Talaga, P., Compound decomposition: A new drug discovery tool?, Drug Discovery Today, 9 (2004) 51–53.CrossRefGoogle Scholar
  321. 321.
    Fenniri, H., Recent advances at the interface of medicinal chemistry and combinatorial chemistry. Views on methodologies for the generation and evaluation of diversity and application to molecular recognition and catalysis, Curr. Med. Chem., 3 (1996) 343–378.Google Scholar
  322. 322.
    Edgar, S.J., Holliday, J.D. and Willett, P., Effectiveness of retrieval in similarity searches of chemical databases: A review of performance measures, J. Molec. Graph. Mod., 18 (2000) 343–357.Google Scholar
  323. 323.
    Stahura, F.L., Xue, L., Godden, J.W. and Bajorath, J., Methods for compound selection focused on hits and application in drug discovery, J. Molec. Graph. Model., 20 (2002) 439–446.Google Scholar
  324. 324.
    Bultinck, P., DeWinter, H., Langenaeker, W., Tollenaere J.P., (Eds.), Computational Medicinal Chemistry for Drug Design, Marcel Dekker Inc., New York, 2003.Google Scholar
  325. 325.
    VanDrie, J.H., 3D Database searching in drug discovery, Network Science. 1996. Available at the following URL: http://www.netsci.org/Science/Cheminform/feature06.html
  326. 326.
    Böhm, H.J. and Stahl, M., Structure-based library design: Molecular modelling merges with combinatorial chemistry, Curr. Op. Chem. Bio., 4 (2000) 283–286.Google Scholar
  327. 327.
    Gorse, D. and Lahana, R., Functional diversity of compounds libraries, Curr. Op. Chem. Bio., 4 (2000) 287–294.Google Scholar
  328. 328.
    Ghosh, A., Computational bioinorganic chemistry. Part III. The tools of the trade: From high-level ab initio calculations to structural bioinformatics, Curr. Op. Chem. Bio., 7 (2003) 110–112 (Parts I and II, have been published in the same journal).Google Scholar
  329. 329.
    Kingston, D.G., Natural products as pharmaceuticals and sources for lead structures, In Wermuth, C.G. (Ed.), The Practice of Medicinal Chemistry, Academic Press, London, 1996.Google Scholar
  330. 330.
    Warr, W.A., Combinatorial chemistry and molecular diversity. An overview, J. Chem. Inf. Comput. Sci., 37 (1997) 134–140.CrossRefGoogle Scholar
  331. 331.
    Reitz, M., Sacher, O., Tarkhov, A., Trümbach, D. and Gasteiger, J., Enabling the exploration of biochemical pathways, Org. Biomol. Chem., 2 (2004) 3226–3237.CrossRefGoogle Scholar
  332. 332.
    Stahura, F.L. and Bajorath, J. Virtual screening methods that complements HTS, Comb. Chem. & HTS, 7 (2004) 259–269.Google Scholar
  333. 333.
    Fliri, A.F., Loging, W.T., Thadeio, P.F. and Volkmann, R.A., Biological spectra analysis: Linking biological activity profiles to molecular structure, PNAS, 102 (2005) 261–266.CrossRefGoogle Scholar
  334. 334.
    Moret, M.A., Miranda, J., Nogueira Jr., E., Santana, M.C. and Zebende, G.F., Self-similarity and protein chains, Physical Review E, 71 (2005) 012901.CrossRefGoogle Scholar
  335. 335.
    Ostberg, N. and Kaznessis, Y., Protegrin structure-activity relationships: Using homology models of synthetic sequences to determine structural characteristics important for activity, Peptides, 26 (2005) 197–206.CrossRefGoogle Scholar
  336. 336.
    Deng, Z., Chuaqui, C. and Singh, J., Structural interaction fingerprint (SIFt): A novel method for analyzing three-dimensional protein-ligand binding interactions, J. Med. Chem., 47 (2004) 337–344.CrossRefGoogle Scholar
  337. 337.
    More information available at the following URL: http://www.ucl.ac.uk/oncology/MicroCore/HTML_resource/tut_frameset.htm
  338. 338.
    Willett, P., Computational tools for the analysis of molecular diversity, Perspectiv. Drug Disc. Design, 7/8 (1997) 1–11.Google Scholar
  339. 339.
    Xue, L., Stahura, F.L. and Bajorath J., Cell-based partitioning, In Bajorath, J. (Ed.) Methods in Molecular Biology, vol. 275. Chemoinformatics. Concepts, Methods and Tools for Drug Discovery. Humana Press Inc., Totowa, NJ, 2004, pp. 279–289.Google Scholar
  340. 340.
    Oprea, T. and Matter, H., Integrating virtual screening in lead discovery, Current Opinion in Chemical Biology, 8 (2004) 349–358.CrossRefGoogle Scholar
  341. 341.
    Hann, M.M. and Oprea, T., Pursuing the leadlikeness concept in pharmaceutical research, Current Opinion in Chemical Biology 8 (2004) 255–263.CrossRefGoogle Scholar
  342. 342.
    Oprea, T., Next-generation therapeutic, Current opinion in Chemical biology, 8 (2004) 347–348.CrossRefGoogle Scholar
  343. 343.
    Zamora, I., Oprea, T., Cruciani, G., Pastor, M. and Ungell, A.L., Surface descriptors for protein ligand affinity prediction, J. Med. Chem. 46 (2003) 25–33.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  • Ana G. Maldonado
    • 1
  • J. P. Doucet
    • 1
  • Michel Petitjean
    • 1
  • Bo-Tao Fan
    • 1
  1. 1.ITODYSUniversité Paris 7 – Denis Diderot, CNRS UMR-7086ParisFrance

Personalised recommendations