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Application of Neural Networks to Large Dataset QSAR, Virtual Screening, and Library Design

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Combinatorial Library

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 201))

Abstract

In the past decade, it became clear that some fundamental problems were arising in drug discovery. It was becoming harder to find new chemical entities with substantial advantages over existing drugs. Consequently, it became riskier and more expensive to develop new drug entities.

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References

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

    Article  PubMed  CAS  Google Scholar 

  2. Chu K. C. (1980) The quantitative analysis of structure-activity relationships. Burger’s Medicinal Chemistry, 4th edit, Ed. M. E. Wolff, Wiley and Son New York, vol. 1, pp. 393–418.

    Google Scholar 

  3. Martin Y. C. (1981) A practitioner’s perspective of the role of quantitative structure-activity analysis in medicinal chemistry. J. Med. Chem. 24, 229–237.

    Article  PubMed  CAS  Google Scholar 

  4. Mager P. P. (1984) Biometrics in medicinal chemistry: a difficult road ahead. QSAR Des. Bioact. Compd., 433–442.

    Google Scholar 

  5. Trinajstic N., Randic M., and Klein D. J. (1986) On the quantitative structureactivity relationship in drug research. Acta Pharm. Jugosl. 36, 267–279.

    CAS  Google Scholar 

  6. Martin Y. C. (1981) A practitioner’s perspective of the role of quantitative structure-activity analysis in medicinal chemistry. J. Med. Chem. 24, 229–237.

    Article  PubMed  CAS  Google Scholar 

  7. Guo Z. (1995) Structure-activity relationships in medicinal chemistry: development of drug candidates from lead compounds. Pharmacochem. Libr. 23, 299–320.

    Article  CAS  Google Scholar 

  8. Karelson M., Lobanov V. S., and Katritzky A. R. (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev. 96, 1027–1043.

    Article  PubMed  CAS  Google Scholar 

  9. Kaiser K. L. E. (1999) Quantitative structure-activity relationships in chemistry. Can. Chem. News 51, 23–24.

    CAS  Google Scholar 

  10. Jackson R. C. (1995) Update on computer-aided drug design. Curr. Opin. Biotech. 6, 646–651.

    Article  PubMed  CAS  Google Scholar 

  11. Salt D. W., Yildiz N., Livingstone D. J., and Tinsley C. J. (1992) The use of artificial neural networks in QSAR. Pestic. Sci. 36, 161–170.

    Article  CAS  Google Scholar 

  12. Hansch C. (1976) On the structure of medicinal chemistry. J. Med. Chem. 19, 1–6.

    Article  PubMed  CAS  Google Scholar 

  13. Winkler D. A. and Madellena D. J. (1995) QSAR and neural networks in life sciences. Ser. Math. Biol. Med. 5, 126–163.

    CAS  Google Scholar 

  14. Peterson K. L. (2000) Artificial neural networks and their use in chemistry. Rev. Comput. Chem. 16, 53–140.

    Article  CAS  Google Scholar 

  15. Sadowski J. (2000) Optimization of chemical libraries by neural networks. Curr. Opin. Chem. Biol. 4, 280–282.

    Article  PubMed  CAS  Google Scholar 

  16. Manallack D. T. and Livingstone D. J. (1999) Neural networks in drug discovery: have they lived up to their promise? Eur. J. Med. Chem. 34, 195–208.

    Article  CAS  Google Scholar 

  17. Schneider G. and Wrede P. (1998) Artificial neural networks for computerbased molecular design. Prog. Biophys. Mol. Biol. 70, 175–222.

    Article  PubMed  CAS  Google Scholar 

  18. Burden F. R. and Winkler D. A. (1999) New QSAR methods applied to structure-activity map** and combinatorial chemistry. J. Chem. Inf. Comput. Sci. 39, 236–242.

    Article  CAS  Google Scholar 

  19. Anzali S., Gasteiger J., Holzgrabe U., Polanski J., Sadowski J., Teckentrup A., and Wagener M. (1998) The use of self-organizing neural networks in drug design. Perspect. Drug Discov. Des. 9, 273–299.

    Article  Google Scholar 

  20. Svozil D., Kvasnicka V., and Pospichal J. (1997) Introduction to multi-layer feedforward neural networks. Chemom. Intell. Lab. Syst. 39, 43–62.

    Article  CAS  Google Scholar 

  21. Zupan J. (1994) Introduction to artificial neural network (ANN) methods: what they are and how to use them. Acta Chim. Slov. 41, 327–352.

    CAS  Google Scholar 

  22. Sumpter B. G., Getino C., and Noid D. W. (1994) Theory and applications of neural computing in chemical science. Annu. Rev. Phys. Chem. 45, 439–481.

    Article  CAS  Google Scholar 

  23. Melssen W. J., Smits J. R. M., Buydens L. M. C., and Kateman G. (1994) Using artificial neural networks for solving chemical problems Part II. Kohonen self-organizing feature maps and Hopfield networks. Chemom. Intell. Lab. Syst. 23, 267–291.

    Article  CAS  Google Scholar 

  24. Smits J. R. M., Melssen W. J., Buydens L. M. C., and Kateman G. (1994) Using artificial neural networks for solving chemical problems. Part I. Multilayer feedforward networks. Chemom. Intell. Lab. Syst. 22, 165–189.

    Article  CAS  Google Scholar 

  25. Lohninger H. (1993) Neural networks in the chemistry. Oesterr. Chem. Z. 94, 90–92.

    CAS  Google Scholar 

  26. Gasteiger J. and Zupan J. (1993) Neural networks in chemistry. Angew. Chem. 105, 510–536 (See also Angew. Chem., Int. Ed. Engl. [1993]), 32, 503−527.

    Article  CAS  Google Scholar 

  27. Wythoff B. J. (1993) Back-propagation neural networks. A tutorial. Chemom. Intell. Lab. Syst. 18, 115–155.

    Article  CAS  Google Scholar 

  28. Tusar M., Zupan J., and Gasteiger J. (1992) Neural networks and modeling in chemistry. J. Chim. Phys. Phys.-Chim. Biol. 89, 1517–1529.

    CAS  Google Scholar 

  29. Schneider G. and Wrede P. (1998) Artificial neural networks for computerbased molecular design. Prog. Biophys. Mol. Biol. 70, 175–222.

    Article  PubMed  CAS  Google Scholar 

  30. Hansch C. and Fujita T. (1964) ρ-σ-π Analysis. A method for correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86, 1616.

    Article  CAS  Google Scholar 

  31. Maddalena D. J. (1996) Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin. Ther. Pat. 6, 239–251.

    Article  CAS  Google Scholar 

  32. Wrotnowski C. (1999) Counting on computational intelligence. Mod. Drug Discov. 2, 46–48, 51−52, 55.

    CAS  Google Scholar 

  33. Maggiora G. M., Elrod D. W., and Trenary R. G. (1992) Computational neural networks as model-free map** devices. J. Chem. Inf. Comput. Sci. 32, 732–741.

    Article  CAS  Google Scholar 

  34. Burns J. A. and Whitesides G. M. (1993) Feedforward neural networks in chemistry: mathematical systems for classification and pattern recognition. Chem. Rev. 93, 2583–2601.

    Article  CAS  Google Scholar 

  35. Balaban A. T. and Basak S. C. (2000) Trends and possibilities for future developments of topological indices. Abstr. Pap. Am. Chem. Soc. 220th, COMP-048.

    Google Scholar 

  36. Sutter J. M. and Jurs P. C. (1995) Selection of molecular descriptors for quantitative structure-activity relationships. Data Handl. Sci. Technol. 15, 111–132.

    Article  CAS  Google Scholar 

  37. Winkler D. A. and Burden F. R. (2000) Robust QSAR models from novel descriptors and Bayesian regularized neural networks. Mol. Simul. 24, 243–258.

    Article  CAS  Google Scholar 

  38. Maddalena D. J. (1998) Applications of soft computing in drug design. Expert Opin. Ther. Pat. 8, 249–258.

    Article  CAS  Google Scholar 

  39. Apostolakis J., and Caflisch A. (1999) Computational ligand deign. Comb. Chem. High-Throughput Screen. 2, 91–104.

    PubMed  CAS  Google Scholar 

  40. Muskal S. M. (1995) Exploiting data from combinatorial synthesis and screening. Abstr. Pap. Am. Chem. Soc. 210th, CINF-023.

    Google Scholar 

  41. Keseru G. M., Molnar L., and Greiner I. (2000) A neural network based virtual high-throughput screening test for the prediction of CNS activity. Comb. Chem. High-Throughput Screen. 3, 535–540.

    PubMed  CAS  Google Scholar 

  42. Lobanov V. S. (2000) High-throughput screening of virtual combinatorial libraries with neural networks. Abstr. Pap. Am. Chem. Soc. 220th, CINF-062.

    Google Scholar 

  43. Gasteiger J., Teckentrup A., and Briem H. (2000) Analyzing high-throughput screening data by neural networks. Abstr. Pap. Am. Chem. Soc. 219th, COMP-124.

    Google Scholar 

  44. Walters W. P., Ajay and Murcko M. A. (1999) Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 3, 384–387.

    Article  PubMed  CAS  Google Scholar 

  45. Sadowski J. and Kubinyi H. (1998) A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329.

    Article  PubMed  CAS  Google Scholar 

  46. Huang G. M., Farkas J., and Hood L. (1996) High-throughput cDNA screening utilizing a low order neural network filter. BioTechniques 21, 1110–1114.

    PubMed  CAS  Google Scholar 

  47. Schwabe W., Lawrence B. J., Robb A. S., Hopfinger R. M., Hochgeschwender U., and Brennan M. B. (1994) Direct cDNA screening of genomic reference libraries—a rapid method for the identification of transcribed sequences in large genomic regions. Identif. Transcrib. Sequences [Proc. Int. Workshop], 3rd, pp. 139–155.

    Google Scholar 

  48. So S.-S. and Karplus M. (1999) A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors. J. Comput.-Aided Mol. Des. 13, 243–258.

    Article  PubMed  CAS  Google Scholar 

  49. Bassett S. I. and Elling J. W. (1998) Automating data analysis for high-throughput screening. Abstr. Pap. Am. Chem. Soc. 216th, CINF-009.

    Google Scholar 

  50. Tronchet J. M. J., Grigorov M., Dolatshahi N., Moriaud F., and Weber J. (1997) A QSAR study confirming the heterogeneity of the HEPT derivative series regarding their interaction with HIV reverse transcriptase. Eur. J. Med. Chem. 32, 279–299.

    Article  CAS  Google Scholar 

  51. Maddalena D. J. and Johnston G. A. R. (1995) Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks. J. Med. Chem. 38, 715–724.

    Article  PubMed  CAS  Google Scholar 

  52. Lipinski C. A. (2001) Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249.

    Article  Google Scholar 

  53. Lipinski C. A. (2000) Changes in the profiles of drug properties: an experimental, computational, and informatics perspective. Abstr. Pap. Am. Chem. Soc. 219th, CINF-020.

    Google Scholar 

  54. Sadowski J. (2000) Fast computational filters for predicting ADME/Tox. Abstr. Pap. Am. Chem. Soc. 219th, CINF-006.

    Google Scholar 

  55. Vedani A. and Dobler M. (2000) Multi-dimensional QSAR in drug research: predicting binding affinities, toxicity and pharmacokinetic parameters. Prog. Drug Res. 55, 105–135.

    Article  PubMed  CAS  Google Scholar 

  56. Gobburu J. V. S. and Shelver W. H. (1995) Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks. J. Pharmaceut. Sci. 84, 862–865.

    Article  CAS  Google Scholar 

  57. Schapper K.-J., Wiese M., Dieter R., Emig P., Engel J., Kutscher B., and Polymeropoulos E. E. (1993) QSAR analysis of time-and dose-dependent in vivo drug effects using artificial neural networks. Trends QSAR Mol. Modell. 92, Proc. Eur. Symp. Structure-Activity Relationships: QSAR and Molecular Modeling 9th, pp. 546–549., Strasbourg France.

    Google Scholar 

  58. Lockwood L., Jr., Breneman C. M., Embrechts M. J., Bennett K. P., and Arciniegas F. (2000) Use of 2D, 3D, TAE and wavelet coefficient descriptors (WCDs) for generating self-organizing Kohonen maps for QSAR, QSPR, and ADME analyses. Abstr. Pap. Am. Chem. Soc. 220th, COMP-134.

    Google Scholar 

  59. Bradbury S. P. (1994) Predicting modes of toxic action from chemical structure: an overview. SAR QSAR Environ. Res. 2, 89–104.

    Article  PubMed  CAS  Google Scholar 

  60. Calleja M. C., Geladi P., and Persoone G. (1994) QSAR models for predicting the acute toxicity of selected organic chemicals with diverse structures to aquatic non-vertebrates and humans. SAR QSAR Environ. Res. 2, 193–234.

    Article  PubMed  CAS  Google Scholar 

  61. Devillers J. and Domine D. (1997) Neural modeling of the Microtox test. Abstr. Pap. Am. Chem. Soc. 213th, COMP-391.

    Google Scholar 

  62. Devillers J., Bintein S., Domine D., and Karcher W. (1995) A general QSAR model for predicting the toxicity of organic chemicals to luminescent bacteria (Microtox test). SAR QSAR Environ. Res. 4, 29–38.

    Article  PubMed  CAS  Google Scholar 

  63. Devillers J. and Domine D. (1999) A noncongeneric model for predicting toxicity of organic molecules to Vibrio fischeri. SAR QSAR Environ. Res. 10, 61–70.

    Article  CAS  Google Scholar 

  64. Xu L., Ball J. W., Dixon S. L., and Jurs P. C. (1994) Quantitative structure-activity relationships for toxicity of phenols using regression analysis and computational neural networks. Environ. Toxicol. Chem. 13, 841–851.

    Article  CAS  Google Scholar 

  65. Basak S. C., Grunwald G. D., Gute B. D., Balasubramanian K., and Opitz D. (2000) Use of statistical and neural net approaches in predicting toxicity of chemicals. J. Chem. Inf. Comput. Sci. 40, 885–890.

    Article  PubMed  CAS  Google Scholar 

  66. Tang G. and Bai N. (1999) QSAR toxicity study of chlorated aliphatic hydrocarbons using genetic neural network GA-ANN approach. Zhongguo Huan**g Kexue 19, 539–543.

    CAS  Google Scholar 

  67. Eldred D. V. and Jurs P. C. (1999) Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure. SAR QSAR Environ. Res. 10, 75–99.

    Article  PubMed  CAS  Google Scholar 

  68. Johnson S. R. and Jurs P. C. (1997) Prediction of acute mammalian toxicity from molecular structure for a diverse set of substituted anilines using regression analysis and computational neural networks. Comput. Assist. Lead Find. Optim., [Eur. Symp. Quant. Structure-Activity Relationships: QSAR and Molecular Modeling], 11th, pp. 31–48, Lausanne Switzerland.

    Google Scholar 

  69. Ivanciuc O. (1998) Artificial neural networks applications. Part 4. Quantitative structure-activity relationships for the estimation of the relative toxicity of phenols for Tetrahymena. Rev. Roum. Chim. 43, 255–260.

    CAS  Google Scholar 

  70. Basak S. C., Grunwald G. D., Host G. E., Niemi G. J., and Bradbury S. P. (1998) A comparative study of molecular similarity, statistical, and neural methods for predicting toxic modes of action. Environ. Toxicol. Chem. 17, 1056–1064.

    Article  CAS  Google Scholar 

  71. Eldred D. V., Weikel C. L., and Jurs P. C. (1999) Prediction of acute fathead minnow toxicity of organic compounds from molecular structure. Abstr. Pap. Am. Chem. Soc. 217th, COMP-054.

    Google Scholar 

  72. Zakarya D., Boulaamail A., Larfaoui E. M., and Lakhlifi T. (1997) QSARs for toxicity of DDT-type analogs using neural network. SAR QSAR Environ. Res. 6, 183–203.

    Article  PubMed  CAS  Google Scholar 

  73. Devillers J., Bintein S., and Domine D. (1996) Predicting the toxicity of chemicals to luminescent bacteria (Microtox test) from linear and non-linear multivariate analyses. Abstr. Pap. Am. Chem. Soc. 211th, COMP-062.

    Google Scholar 

  74. Eldred D. V., Weikel C. L., Jurs P. C., and Kaiser K. L. (1999) Prediction of fathead minnow acute toxicity of organic compounds from molecular structure. Chem. Res. Toxicol. 12, 670–678.

    Article  PubMed  CAS  Google Scholar 

  75. Shi L. M., Fan Y., Lee J. K., Waltham M., Andrews D. T., Scherf U., et al. (2000) Mining and visualizing large anticancer drug discovery databases. J. Chem. Inf. Comput. Sci. 40, 367–379.

    Article  PubMed  CAS  Google Scholar 

  76. Shi L. M., Fan Y., Myers T. G., and Weinstein J. N. (1997) Genetic function approximation in the molecular pharmacology of cancer. Proc. Int. Conf. Neural Networks 4, 2490–2493.

    CAS  Google Scholar 

  77. van Osdol W. W., Myers T. G., Paull K. D., Kohn K. W., and Weinstein J. N. (1994) Use of the Kohonen self-organizing map to study the mechanisms of action of chemotherapeutic agents. J. Natl. Cancer Inst. 86, 1853–1859.

    Article  PubMed  Google Scholar 

  78. Weinstein J. N., Kohn K. W., Grever M. R., Viswanadhan V. N., Rubinstein L. V., Monks A. P., et al. (1992) Neural computing in cancer drug development: predicting mechanism of action. Science 258, 447–451.

    Article  PubMed  CAS  Google Scholar 

  79. Weinstein J. N., Myers T., Buolamwini J., Raghavan K., van Osdol W., Licht J., et al. (1994) Predictive statistics and artificial intelligence in the U. S. National Cancer Institute’s Drug Discovery Program for Cancer and AIDS. Stem Cells 12, 13–22.

    Article  PubMed  CAS  Google Scholar 

  80. Benigni R. and Richard A. M. (1996) QSARS of mutagens and carcinogens: two case studies illustrating problems in the construction of models for noncongeneric chemicals. Mutat. Res. 371, 29–46.

    Article  PubMed  CAS  Google Scholar 

  81. Burden F. R. and Winkler D. A. (2000) A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem. Res. Toxicol. 13, 436–440.

    Article  PubMed  CAS  Google Scholar 

  82. Song X.-H., **ao M., and Yu R.-Q. (1994) Artificial neural networks applied to classification of mutagenic activity of nitro-substituted polycyclic aromatic hydrocarbons. Comput. Chem. 18, 391–396.

    Article  PubMed  CAS  Google Scholar 

  83. Villemin D., Cherqaoui D., and Cense J. M. (1993) Neural networks studies: quantitative structure-activity relationship of mutagenic aromatic nitro compounds. J. Chim. Phys. Phys. Chim. Biol. 90, 1505–1519.

    CAS  Google Scholar 

  84. Karelson M., Sild S., and Maran U. (2000) Non-linear QSAR treatment of genotoxicity. Mol. Simul. 24, 229–242.

    Article  CAS  Google Scholar 

  85. Ghoshal N., Mukhopadhayay S. N., Ghoshal T. K., and Achari B. (1993) Quantitative structure-activity relationship studies using artificial neural networks. Indian J. Chem. Sect. B 32B, 1045–1050.

    CAS  Google Scholar 

  86. Benigni R. and Giuliani A. (1994) Quantitative structure-activity relationship (QSAR) studies in genetic toxicology: mathematical models and the “biological activity” term of the relationship. Mutat. Res. 306, 181–186.

    Article  PubMed  CAS  Google Scholar 

  87. Ghoshal N., Mukhopadhyay S. N., Ghoshal T. K., and Achari B. (1993) Quantitative structure-activity relationship studies of aromatic and heteroaromatic nitro compounds using neural network. Bioorg. Med. Chem. Lett. 3, 329–332.

    Article  CAS  Google Scholar 

  88. Vracko M. (1997) A study of structure-carcinogenic potency relationship with rtificial neural networks. The use of descriptors related to geometrical and electronic structures. J. Chem. Inf. Comput. Sci. 37, 1037–1043.

    Article  CAS  Google Scholar 

  89. Benigni R. and Pino A. (1998) Profiles of chemically-induced tumors in rodents: quantitative relationships. Mutat. Res. 421, 93–107.

    Article  PubMed  CAS  Google Scholar 

  90. Barratt M. D. (1996) Quantitative structure-activity relationships (QSARs) for skin corrosivity of organic acids, bases and phenols: principal components and neural network analysis of extended datasets. Toxicol. In Vitro 10, 85–94.

    Article  PubMed  CAS  Google Scholar 

  91. Barratt M. D., Dixit M. B., and Jones P. A. (1996) The use of in vitro cytotoxicity measurements in QSAR methods for the prediction of the skin corrosivity potential of acids. Toxicol. In Vitro 10, 283–290.

    Article  PubMed  CAS  Google Scholar 

  92. Barratt M. D. (1997) QSARS for the eye irritation potential of neutral organic chemicals. Toxicol. In Vitro 11, 1–8.

    Article  PubMed  CAS  Google Scholar 

  93. Patlewicz G. Y., Rodford R. A., Ellis G., and Barratt M. D. (2000) A QSAR model for the eye irritation of cationic surfactants. Toxicol. In Vitro 14, 79–84.

    Article  PubMed  CAS  Google Scholar 

  94. Agrafiotis D. K. and Lobanov V. S. (2000) Non-linear map** networks. J. Chem. Inf. Comput. Sci. 40, 1356–1362.

    Article  PubMed  CAS  Google Scholar 

  95. Rassokhin D. N., Lobanov V. S., and Agrafiotis D. K. (2001) Non-linear map** of massive data sets by fuzzy clustering and neural networks. J. Comput. Chem. 22, 373–386.

    Article  CAS  Google Scholar 

  96. McGregor M. J. and Muskal S. M. (2000) Pharmacophore fingerprinting in QSAR and primary library design. Patent WO 99-US25460, US 98-106007.

    Google Scholar 

  97. Ajay, Bemis G. W. and Murcko M. A. (1999) Designing libraries with CNS activity. J. Med. Chem. 42, 4942–4951.

    Article  PubMed  CAS  Google Scholar 

  98. Wrede P., Schneider G., and Mueller G. (1995) Molecular bioinformatics: Evolutionary peptide design in machina. Bioforum 18, 296–297, 300−303.

    CAS  Google Scholar 

  99. Ivanciuc O. (1999) Molecular graph descriptors used in neural network models. Department of Organic Chemistry, University “Politehnica” of Bucharest, Rom. Editor(s): Devillers, James, Balaban, Alexandru T. Topol Indices Relat. Descriptors QSAR QSPR (1999), pp. 697–777. Publisher: Gordon and Breach Science Publishers Amsterdam, Neth.

    Google Scholar 

  100. Bacherikov V. A. and Shapiro Y. E. (1998) Zefirov-Palyulin analysis of the form of the cyclohexanone fragment of 2-arylidene-and 2-(O-aroyl)oxymethylene-pmenthan-3-ones. J. Struct. Chem. 38, 947–953.

    Article  Google Scholar 

  101. Kireev D. B. (1995) ChemNet: a novel neural network-based method for graph/property map**. J. Chem. Inf. Comput. Sci. 35, 175–80.

    Article  CAS  Google Scholar 

  102. Duprat A. F., Huynh T., and Dreyfus G. (1998) Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of LogP. J. Chem. Inf. Comput. Sci. 38, 586–594.

    Article  PubMed  CAS  Google Scholar 

  103. Huuskonen J. J., Livingstone D. J., and Tetko I. V. (2000) Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices. J. Chem. Inf. Comput. Sci. 40, 947–955.

    Article  PubMed  CAS  Google Scholar 

  104. Devillers J., Domine D., Guillon C., and Karcher W. (1998) Simulating lipophilicity of organic molecules with a back-propagation neural network. J. Pharmaceut. Sci. 87, 1086–1090.

    Article  CAS  Google Scholar 

  105. Beck B., Breindl A., and Clark T. (2000) QM/NN QSPR models with error estimation: vapor pressure and logP. J. Chem. Inf. Comput. Sci. 40, 1046–1051.

    Article  PubMed  CAS  Google Scholar 

  106. Bodor N., Huang M.-J., and Harget A. (1994) Neural network studies. Part 3. Prediction of partition coefficients. Theochem 115, 259–266.

    Article  CAS  Google Scholar 

  107. Breindl A., Beck B., Clark T., and Glen R. C. (1997) Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network. J. Mol. Model. 3, 142–155.

    Article  CAS  Google Scholar 

  108. Gakh A. A., Gakh E. G., Sumpter B. G., and Noid D. W. (1994) Neural network-graph theory approach to the prediction of the physical properties of organic compounds. J. Chem. Inf. Comput. Sci. 34, 832–839.

    Article  CAS  Google Scholar 

  109. Huuskonen J. J., Villa A. E. P., and Tetko I. V. (1999) Prediction of partition coefficient based on atom-type electrotopological state indices. J. Pharmaceut. Sci. 88, 229–233.

    Article  CAS  Google Scholar 

  110. Huuskonen J. (2000) Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J. Chem. Inf. Comput. Sci. 40, 773–777.

    Article  PubMed  CAS  Google Scholar 

  111. Clark T., Beck B., Breindl A., and Hennemann M. (1999) The use of quantum mechanics for QSPR and QSAR models with error estimation. Abstr. Pap. Am. Chem. Soc. 217th, COMP-224.

    Google Scholar 

  112. Goll E. S. and Jurs P. C. (1999) Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model. J. Chem. Inf. Comput. Sci. 39, 974–983.

    Article  CAS  Google Scholar 

  113. Cherqaoui D. and Villemin D. (1994) Use of a neural network to determine the boiling point of alkanes. J. Chem. Soc. Faraday Trans. 90, 97–102.

    Article  CAS  Google Scholar 

  114. Tetteh J., Suzuki T., Metcalfe E., and Howells S. (1999) Quantitative structure-property relationships for the estimation of boiling point and flash point using a radial basis function neural network. J. Chem. Inf. Comput. Sci. 39, 491–507.

    Article  CAS  Google Scholar 

  115. Burden F. R. (1996) Using artificial neural networks to predict biological activity from simple molecular structural considerations. Quant. Struct. Act. Relat. 15, 7–11.

    Article  CAS  Google Scholar 

  116. Winkler D. A., Burden F. R., and Watkins A. J. R. (1998) Atomistic topological indices applied to benzodiazepines using various regression methods. Quant. Struct. Act. Relat. 17, 14–19.

    Article  CAS  Google Scholar 

  117. Burden F. R. (1989) Molecular identification number for substructure searches. J. Chem. Inf. Comput. Sci. 29, 225–227.

    Article  CAS  Google Scholar 

  118. Burden F. R. (1997) A chemically intuitive molecular index based on the eigenvalues of a modified adjacency matrix. Quant. Struct. Act. Relat. 16, 309–314.

    Article  CAS  Google Scholar 

  119. Stanton D. T. (1999) Evaluation and use of BCUT descriptors in QSAR and QSPR studies. J. Chem. Inf. Comput. Sci. 39, 11–20.

    CAS  Google Scholar 

  120. Pirard B. and Pickett S. D. (2000) Classification of kinase inhibitors using BCUT descriptors. J. Chem. Inf. Comput. Sci. 40, 1431–1440.

    Article  PubMed  CAS  Google Scholar 

  121. Kubinyi H. (1994) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant. Struct. Act. Relat. 13, 285–294.

    CAS  Google Scholar 

  122. So S.-S. and Karplus M. (1997) Three-dimensional quantitative structureactivity relationships from molecular similarity matrixes and genetic neural networks. 1. Method and validations. J. Med. Chem. 40, 4347–4359.

    Article  PubMed  CAS  Google Scholar 

  123. So S.-S. and Karplus M. (1997) Three-dimensional quantitative structureactivity relationships from molecular similarity matrixes and genetic neural networks. 2. Applications. J. Med. Chem. 40, 4360–4371.

    Article  PubMed  CAS  Google Scholar 

  124. So S.-S. and Karplus M. (1996) Genetic neural networks for quantitative structureactivity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors. J. Med. Chem. 39, 5246–5256.

    Article  PubMed  CAS  Google Scholar 

  125. So S.-S. and Karplus M. (1996) Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. J. Med. Chem. 39, 1521–1530.

    Article  PubMed  CAS  Google Scholar 

  126. Luke B. T. (1994) Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 34, 1279–1287.

    Article  CAS  Google Scholar 

  127. Waller C. L. and Bradley M. P. (1999) Development and validation of a novel variable selection technique with application to multidimensional quantitative structure-activity relationship studies. J. Chem. Inf. Comput. Sci. 39, 345–355.

    Article  CAS  Google Scholar 

  128. Zupan J. and Novic M. (1999) Optimization of structure representation for QSAR studies. Analyt. Chim. Acta 388, 243–250.

    Article  CAS  Google Scholar 

  129. Neal R. M. (1996) Bayesian learning for neural networks, vol. 118, Springer-Verlag Berlin.

    Book  Google Scholar 

  130. Burden F. R., Ford M. G., Whitley D. C., and Winkler D. A. (2000) Use of automatic relevance determination in QSAR studies using Bayesian neural networks. J. Chem. Inf. Comput. Sci. 40, 1423–1430.

    Article  PubMed  CAS  Google Scholar 

  131. Kovalishyn V. V., Tetko I. V., Luik A. I., Kholodovych V. V., Villa A. E. P., and Livingstone D. J. (1998) Neural network studies. 3. Variable selection in the cascade-correlation learning architecture. J. Chem. Inf. Comput. Sci. 38, 651–659.

    Article  CAS  Google Scholar 

  132. Sutter J. M., Dixon S. L., and Jurs P. C. (1995) Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comput. Sci. 35, 77–84.

    Article  CAS  Google Scholar 

  133. MacKay D. J. C. (1992) Bayesian interpolation. Neural Computat. 4, 415–447.

    Article  Google Scholar 

  134. MacKay D. J. C. (1992) A practical Bayesian framework for back-propagation networks. Neural Computat. 4, 448–472.

    Article  Google Scholar 

  135. Burden F. R. and Winkler D. A. (1999) Robust QSAR models using Bayesian regularized neural networks. J. Med. Chem. 42, 3183–3187.

    Article  PubMed  CAS  Google Scholar 

  136. Niculescu S. P., Kaiser K. L. E., and Schuurmann G. (1998) Influence of data preprocessing and kernel selection on probabilistic neural network modeling of the acute toxicity of chemicals to the fathead minnow and Vibrio fischeri bacteria. Water Qual. Res. J. Can. 33, 153–165.

    CAS  Google Scholar 

  137. Doucet J. P. and Panaye A. (1998) 3D Structural information: from property prediction to substructure recognition with neural networks. SAR QSAR Environ. Res. 8, 249–272.

    Article  CAS  Google Scholar 

  138. Feuilleaubois E., Fabart V., and Doucet J. P. (1993) Implementation of the three-dimensional-pattern search problem on Hopfield-like neural networks. SAR QSAR Environ. Res. 1, 97–114.

    Article  PubMed  CAS  Google Scholar 

  139. Campbell J. L. E. and Johnson K. E. (1993) Abductive networks: generalization, pattern recognition, and prediction of chemical behavior. Can. J. Chem. 71, 1800–1804.

    Article  CAS  Google Scholar 

  140. Burden F. R. (1998) Holographic neural networks as non-linear discriminants for chemical applications. J. Chem. Inf. Comput. Sci. 38, 47–53.

    Article  PubMed  CAS  Google Scholar 

  141. Gobburu J. V. S., Shelver W. H., and Chen E. P. (1996) Initial assessment of generalized regression neural networks (GRNN) in QSAR. Abstr. Pap. Am. Chem. Soc. 212th, MEDI-004.

    Google Scholar 

  142. Domine D., Devillers J., Wienke D., and Buydens L. (1996) The heuristic potency of art networks for QSAR data visualization and interpretation. Abstr. Pap. Am. Chem. Soc. 211th, COMP-108.

    Google Scholar 

  143. Domine D., Devillers J., Wienke D., and Buydens L. (1997) ART 2-A for Optimal Test Series Design in QSAR. J. Chem. Inf. Comput. Sci. 37, 10–17.

    Article  CAS  Google Scholar 

  144. Micheli A., Sperduti A., Starita A., and Bianucci A. M. (2001) Analysis of the internal representations developed by neural networks for structures applied to quantitative structure-activity relationship studies of benzodiazepines. J. Chem. Inf. Comput. Sci. 41, 202–218.

    Article  PubMed  CAS  Google Scholar 

  145. Kyngas J. and Valjakka J. (1996) Evolutionary neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. Quant. Struct. Act. Relat. 15, 296–301.

    Article  CAS  Google Scholar 

  146. Kireev D. B., Ros F., Bernard P., Chretien J. R., and Rozhkova N. (1997) Non-supervised neural networks: a new classification tool to process large databases. Comput.-Assisted Lead Find. Optim. [Eur. Symp. Quant. Struct. Act. Relat.], 11th, pp. 255–264.

    Google Scholar 

  147. Bernard P., Golbraikh A., Kireev D., Chretien J. R,. and Rozhkova N. (1998) Comparison of chemical databases: analysis of molecular diversity with selforganizing maps (SOM). Analysis 26, 333–341.

    Article  CAS  Google Scholar 

  148. Bienfait B. (1994) Applications of high-resolution self-organizing maps to retrosynthetic and QSAR analysis. J. Chem. Inf. Comput. Sci. 34, 890–898.

    Article  CAS  Google Scholar 

  149. Gasteiger J., Wagener M., and Sadowski J. (1996) Assessment of the diversity of combinatorial libraries by an encoding of molecular surface properties. Abstr. Pap. Am. Chem. Soc. 211th, CINF-070.

    Google Scholar 

  150. Hanke J. and Reich J. G. (1996) Kohonen map as a visualization tool for the analysis of protein sequences: multiple alignments, domains and segments of secondary structures. Comput. Appl. Biosci. 12, 447–454.

    PubMed  CAS  Google Scholar 

  151. Rose V. S., Macfie H. J. H., and Croall I. F. (1991) Kohonen topologypreserving map**: an unsupervised artificial neural network method for use in QSAR analysis. Pharmacochem. Libr. 16, 213–216.

    CAS  Google Scholar 

  152. Rose V. S., Croall I. F., and MacFie H. J. H. (1991) An application of unsupervised neural network methodology (Kohonen topology-preserving map**) to QSAR analysis. Quant. Struct. Act. Relat. 10, 6–15.

    Article  CAS  Google Scholar 

  153. Shi L. M., Myers T. G., Fan Y., and Weinstein J. N. (1997) Mining the NCI anticancer drug screen database using genetic function approximation (GFA) and cluster analysis. Abstr. Pap. Am. Chem. Soc. 213th, COMP-214.

    Google Scholar 

  154. Burden F. R., Rosewarne B. S., and Winkler D. A. (1997) Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms. Chemom. Intell. Lab. Syst. 38, 127–137.

    Article  CAS  Google Scholar 

  155. Devillers J. (1996) Designing molecules with specific properties from intercommunicating hybrid systems. J. Chem. Inf. Comput. Sci. 36, 1061–1066.

    Article  PubMed  CAS  Google Scholar 

  156. Jiang J.-H., Wang J.-H., Song X.-H., and Yu R.-Q. (1996) Network training and architecture optimization by a recursive approach and a modified genetic algorithm. J. Chemom. 10, 253–267.

    Article  CAS  Google Scholar 

  157. Mager P. P. and Reinhardt R. (1999) A comparison of back-propagation and generalized-regression genetic-neural network models. Drug Des. Discov. 16, 49–53.

    PubMed  CAS  Google Scholar 

  158. Liu Q., Hirono S., and Moriguchi I. (1992) Application of functional-link net in QSAR. 2. QSAR for activity data given by ratings. Quant. Struct. Act. Relat. 11, 318–324.

    Article  CAS  Google Scholar 

  159. Manallack D. T. and Livingstone D. J. (1994) Limitations of functional-link nets as applied to QSAR data analysis. Quant. Struct. Act. Relat. 13, 18–21.

    CAS  Google Scholar 

  160. Livingstone D. J., Hesketh G., and Clayworth D. (1991) Novel method for display of multivariate data using neural networks. J. Mol. Graphics 9, 115–118.

    Article  CAS  Google Scholar 

  161. Reibnegger G., Werner-Felmayer G., and Wachter H. (1993) A note on the low-dimensional display of multivariate data using neural networks. J. Mol. Graphics 11, 129–133.

    Article  CAS  Google Scholar 

  162. Tetko I. V., Aksenova T. I., Volkovich V. V., Kasheva T. N., Filipov D. V., Welsh W. J., et al. (2000) Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the Internet. SAR QSAR Environ. Res. 11, 263–280.

    Article  PubMed  CAS  Google Scholar 

  163. Manallack D. T. and Livingstone D. J. (1995) Relating biological activity to chemical structure using neural networks. Pestic. Sci. 45, 167–170.

    Article  CAS  Google Scholar 

  164. Manallack D. T. and Livingstone D. J. (1995) Neural networks and expert systems in molecular design. Methods Princ. Med. Chem. 3, 293–318.

    CAS  Google Scholar 

  165. Manallack D. T., Ellis D. D., and Livingstone D. J. (1994) Analysis of linear and non-linear QSAR data using neural networks. J. Med. Chem. 37, 3758–3767.

    Article  PubMed  CAS  Google Scholar 

  166. Livingstone D. J. and Salt D. W. (1992) Regression analysis for QSAR using neural networks. Bioorg. Med. Chem. Lett. 2, 213–218.

    Article  CAS  Google Scholar 

  167. Kovesdi I., Dominguez-Rodriguez M. F., Orfi L., Naray-Szabo G., Varro A., Papp J. G., and Matyus P. (1999) Application of neural networks in structure-activity relationships. Med. Res. Rev. 19, 249–269.

    Article  PubMed  CAS  Google Scholar 

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  1. CSIRO Molecular Science, Clayton, Australia

    David A. Winkler

  2. SciMetrics, Harrow Enterprises Ltd, Victoria, Australia

    Frank R. Burden

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  1. David A. Winkler
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  1. BD Biosciences, San Jose, California

    Lisa Bellavance English

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Winkler, D.A., Burden, F.R. (2002). Application of Neural Networks to Large Dataset QSAR, Virtual Screening, and Library Design. In: English, L.B. (eds) Combinatorial Library. Methods in Molecular Biology™, vol 201. Springer, Totowa, NJ. https://doi.org/10.1385/1-59259-285-6:325

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  • DOI: https://doi.org/10.1385/1-59259-285-6:325

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  • Print ISBN: 978-0-89603-980-3

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