Encyclopedia of Complexity and Systems Science

2009 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Drug Design with Machine Learning

  • Ovidiu Ivanciuc
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30440-3_135

Definition of the Subject

The process of drug discovery has the goal to identify lead chemicals that have a significant activity against a selected biological target. A disease state may be the result of changes in the structure and function of cell‐signaling receptors, enzymes, hormone receptors, or other functional proteins. The drug target is a protein whose activity is modulated by its interaction with a chemical compound, and thus may control a disease. The lead compounds identified in the drug discovery step are optimized in the drug development phase that results in a small number of chemicals that are evaluated in human clinical trials. The first priority in drug development is to increase the biological activity of a lead compound while preserving its drug‐like properties. The lead compound is expanded into a chemical library that conserves the structure responsible for the biological activity (pharmacophore) and adds chemical groups that might improve its activity. Then the...

This is a preview of subscription content, log in to check access.


  1. 1.
    Aha DW, Kibler D, Albert MK (1991) Instance‐based learning algorithms. Mach Learn 6:37–66Google Scholar
  2. 2.
    Ajmani S, Jadhav K, Kulkarni SA (2006) Three‐dimensional QSAR using the k‑nearest neighbor method and its interpretation. J Chem Inf Model 46:24–31Google Scholar
  3. 3.
    Andres C, Hutter MC (2006) CNS permeability of drugs predicted by a decision tree. QSAR Comb Sci 25:305–309Google Scholar
  4. 4.
    Alpaydin E (2004) Introduction to machine learning. MIT Press, Cambridge, p 445Google Scholar
  5. 5.
    Atkeson CG, Moore AW, Schaal S (1997) Locally weighted learning. Artif Intell Rev 11:11–73Google Scholar
  6. 6.
    Atkeson CG, Moore AW, Schaal S (1997) Locally weighted learning for control. Artif Intell Rev 11:75–113Google Scholar
  7. 7.
    Arimoto R, Prasad MA, Gifford EM (2005) Development of CYP3A4 inhibition models: comparisons of machine‐learning techniques and molecular descriptors. J Biomol Screen 10:197–205Google Scholar
  8. 8.
    Balaban AT, Ivanciuc O (1999) Historical development of topological indices. In: Devillers J, Balaban AT (eds) Topological indices and related descriptors in QSAR and QSPR. Gordon & Breach Science Publishers, Amsterdam, pp 21–57Google Scholar
  9. 9.
    Basak SC, Grunwald GD (1995) Molecular similarity and estimation of molecular properties. J Chem Inf Comput Sci 35:366–372Google Scholar
  10. 10.
    Basak SC, Bertelsen S, Grunwald GD (1994) Application of graph theoretical parameters in quantifying molecular similarity and structure‐activity relationships. J Chem Inf Comput Sci 34:270–276Google Scholar
  11. 11.
    Basak SC, Bertelsen S, Grunwald GD (1995) Use of graph theoretic parameters in risk assessment of chemicals. Toxicol Lett 79:239–250Google Scholar
  12. 12.
    Bayes T (1763) An essay towards solving a problem in the doctrine of chances. Philos Trans Roy Soc London 53:370–418Google Scholar
  13. 13.
    Bender A, Jenkins JL, Glick M, Deng Z, Nettles JH, Davies JW (2006) “Bayes affinity fingerprints” improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept? J Chem Inf Model 46:2445–2456Google Scholar
  14. 14.
    Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, Urban L, Whitebread S, Jenkins JL (2007) Analysis of pharmacology data and the prediction of adverse drug reactions and off‐target effects from chemical structure. Chem Med Chem 2:861–873Google Scholar
  15. 15.
    Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin, p 740zbMATHGoogle Scholar
  16. 16.
    Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford, p 504zbMATHGoogle Scholar
  17. 17.
    Boid DB (2007) How computational chemistry became important in the pharmaceutical industry. In: Lipkowitz KB, Cundari TR (eds) Reviews in computational chemistry, vol 23. Wiley, Weinheim, pp 401–451Google Scholar
  18. 18.
    Bonchev D (1983) Information theoretic indices for characterization of chemical structure. Research Studies Press, ChichesterGoogle Scholar
  19. 19.
    Bonchev D, Rouvray DH (eds) (1991) Chemical graph theory. Introduction and fundamentals. Abacus Press/Gordon & Breach Science Publishers, New YorkzbMATHGoogle Scholar
  20. 20.
    Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proc of the 5th annual ACM workshop on computational learning theory. ACM Press, Pittsburgh, pp 144–152Google Scholar
  21. 21.
    Bottou L, Chapelle O, DeCoste D, Weston J (2007) Large‐scale kernel machines. MIT Press, Cambridge, p 416Google Scholar
  22. 22.
    Breiman L (2001) Random forests. Mach Learn 45:5–32zbMATHGoogle Scholar
  23. 23.
    Briem H, Günther J (2005) Classifying “kinase inhibitor‐likeness” by using machine‐learning methods. Chem Bio Chem 6:558–566Google Scholar
  24. 24.
    Cash GG (1999) Prediction of physicochemical properties from Euclidean distance methods based on electrotopological state indices. Chemosphere 39:2583–2591Google Scholar
  25. 25.
    Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram‐based image classification. IEEE Trans Neural Netw 10:1055–1064Google Scholar
  26. 26.
    Cleary JG, Trigg LE (1995) K : an instance‐based learner using and entropic distance measure. In: Prieditis A, Russell SJ (eds) Proc of the 12th international conference on machine learning. Morgan Kaufmann, Tahoe City, pp 108–114Google Scholar
  27. 27.
    Cohen WW (1995) Fast effective rule induction. In: Prieditis A, Russell SJ (eds) Proc of the 12th international conference on machine learning. Morgan Kaufmann, Tahoe City, pp 115–123Google Scholar
  28. 28.
    Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297zbMATHGoogle Scholar
  29. 29.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, CambridgeGoogle Scholar
  30. 30.
    DeconinckE, Zhang MH, Coomans D, Vander Heyden Y (2006) Classification treemodels for the prediction of blood-brain barrier passage ofdrugs. J Chem Inf Model 46:1410–1419Google Scholar
  31. 31.
    Deng Z, Chuaqui C, Singh J (2006) Knowledge‐based design of target‐focused libraries using protein‐ligand interaction constraints. J Med Chem 49:490–500Google Scholar
  32. 32.
    Doddareddy MR, Cho YS, Koh HY, Kim DH, Pae AN (2006) In silico renal clearance model using classical Volsurf approach. J Chem Inf Model 46:1312–1320Google Scholar
  33. 33.
    Drucker H, Wu DH, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10:1048–1054Google Scholar
  34. 34.
    DuH, Wang J, Watzl J, Zhang X, Hu Z (2008) Classificationstructure‐activity relationship (CSAR) studies forprediction ofgenotoxicity of thiophene derivatives. Toxicol Lett177:10–19Google Scholar
  35. 35.
    Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley, New YorkGoogle Scholar
  36. 36.
    Ehrman TM, Barlow DJ, Hylands PJ (2007) Virtual screening of chinese herbs with random forest. J Chem Inf Model 47:264–278Google Scholar
  37. 37.
    Eitrich T, Kless A, Druska C, Meyer W, Grotendorst J (2007) Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques. J Chem Inf Model 47:92–103Google Scholar
  38. 38.
    Ekins S, Balakin KV, Savchuk N, Ivanenkov Y (2006) Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitioning and Kohonen and Sammon mapping techniques. J Med Chem 49:5059–5071Google Scholar
  39. 39.
    Ertl P, Roggo S, Schuffenhauer A (2008) Natural product‐likeness score and its application for prioritization of compound libraries. J Chem Inf Model 48:68–74Google Scholar
  40. 40.
    Fatemi MH, Gharaghani S (2007) A novel QSAR model for prediction of apoptosis‐inducing activity of 4-aryl-4-H‑chromenes based on support vector machine. Bioorg Med Chem 15:7746–7754Google Scholar
  41. 41.
    Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20:2479–2481Google Scholar
  42. 42.
    Freund Y, Mason L (1999) The alternating decision tree learning algorithm. In: Bratko I, Dzeroski S (eds) Proc of the 16th international conference on machine learning (ICML (1999)). Morgan Kaufmann, Bled, pp 124–133Google Scholar
  43. 43.
    Gaines BR, Compton P (1995) Induction of ripple‐down rules applied to modeling large databases. Intell J Inf Syst 5:211–228Google Scholar
  44. 44.
    Gao JB, Gunn SR, Harris CJ (2003) SVM regression through variational methods and its sequential implementation. Neurocomputing 55:151–167Google Scholar
  45. 45.
    Gao JB, Gunn SR, Harris CJ (2003) Mean field method for the support vector machine regression. Neurocomputing 50:391–405zbMATHGoogle Scholar
  46. 46.
    Gepp MM, Hutter MC (2006) Determination of hERG channel blockers using a decision tree. Bioorg Med Chem 14:5325–5332Google Scholar
  47. 47.
    Guha R, Dutta D, Jurs PC, Chen T (2006) Local lazy regression: making use of the neighborhood to improve QSAR predictions. J Chem Inf Model 46:1836–1847Google Scholar
  48. 48.
    Gute BD, Basak SC (2001) Molecular similarity‐based estimation of properties: a comparison of three structure spaces. J Mol Graph Modell 20:95–109Google Scholar
  49. 49.
    Gute BD, Basak SC, Mills D, Hawkins DM (2002) Tailored similarity spaces for the prediction of physicochemical properties. Internet Electron J Mol Des 1:374–387Google Scholar
  50. 50.
    Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422zbMATHGoogle Scholar
  51. 51.
    Hansch C, Garg R, Kurup A, Mekapati SB (2003) Allosteric interactions and QSAR: on the role of ligand hydrophobicity. Bioorg Med Chem 11:2075–2084Google Scholar
  52. 52.
    Hastie T, Tibshirani R, Friedman JH (2003) The elements of statistical learning. Springer, Berlin, p 552Google Scholar
  53. 53.
    Herbrich R (2002) Learning kernel classifiers. MIT Press, CambridgeGoogle Scholar
  54. 54.
    Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2006) New methods for ligand‐based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model 46:462–470Google Scholar
  55. 55.
    Hoffman B, Cho SJ, Zheng W, Wyrick S, Nichols DE, Mailman RB, Tropsha A (1999) Quantitative structure‐activity relationship modeling of dopamine \( {\text{D}}_{1} \) antagonists using comparative molecular field analysis, genetic algorithms‐partial least‐squares, and K‑nearest neighbor methods. J Med Chem 42:3217–3226Google Scholar
  56. 56.
    HolteRC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn11:63–90Google Scholar
  57. 57.
    Hou T, Wang J, Zhang W, Xu X (2007) ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification. J Chem Inf Model 47:208–218Google Scholar
  58. 58.
    Huang T-M, Kecman V, Kopriva I (2006) Kernel based algorithms for mining huge data sets. Springer, Berlin, p 260zbMATHGoogle Scholar
  59. 59.
    Hudelson MG, Ketkar NS, Holder LB, Carlson TJ, Peng C-C, Waldher BJ, Jones JP (2008) High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods. J Med Chem 51:648–654Google Scholar
  60. 60.
    Itskowitz P, Tropsha A (2005) k‑nearest neighbors QSAR modeling as a variational problem: theory and applications. J Chem Inf Model 45:777–785Google Scholar
  61. 61.
    Ivanciuc O (2002) Support vector machine classification of the carcinogenic activity of polycyclic aromatic hydrocarbons. Internet Electron J Mol Des 1:203–218Google Scholar
  62. 62.
    Ivanciuc O (2002) Structure‐odor relationships for pyrazines with support vector machines. Internet Electron J Mol Des 1:269–284Google Scholar
  63. 63.
    Ivanciuc O (2002) Support vector machine identification of the aquatic toxicity mechanism of organic compounds. Internet Electron J Mol Des 1:157–172Google Scholar
  64. 64.
    Ivanciuc O (2003) Graph theory in chemistry. In: Gasteiger J (ed) Handbook of chemoinformatics, vol 1. Wiley, Weinheim, pp 103–138Google Scholar
  65. 65.
    Ivanciuc O (2003) Topological indices. In: Gasteiger J (ed) Handbook of chemoinformatics, vol 3. Wiley, Weinheim, pp 981–1003Google Scholar
  66. 66.
    Ivanciuc O (2003) Aquatic toxicity prediction for polar and nonpolar narcotic pollutants with support vector machines. Internet Electron J Mol Des 2:195–208Google Scholar
  67. 67.
    Ivanciuc O (2004) Support vector machines prediction of the mechanism of toxic action from hydrophobicity and experimental toxicity against pimephales promelas and tetrahymena pyriformis. Internet Electron J Mol Des 3:802–821Google Scholar
  68. 68.
    Ivanciuc O (2005) Support vector regression quantitative structure‐activity relationships (QSAR) for benzodiazepine receptor ligands. Internet Electron J Mol Des 4:181–193Google Scholar
  69. 69.
    Ivanciuc O (2005) Machine learning applied to anticancer structure‐activity relationships for NCI human tumor cell lines. Internet Electron J Mol Des 4:948–958Google Scholar
  70. 70.
    Ivanciuc O (2007) Applications of support vector machines in chemistry. In: Lipkowitz KB, Cundari TR (eds) Reviews in computational chemistry, vol 23. Wiley, Weinheim, pp 291–400Google Scholar
  71. 71.
    John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Besnard P, Hanks S (eds) UAI '95: Proc of the 11th annual conference on uncertainty in artificial intelligence. Morgan Kaufmann, Montreal, pp 338–345Google Scholar
  72. 72.
    Jorissen RN, Gilson MK (2005) Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 45:549–561Google Scholar
  73. 73.
    Jurs P (2003) Quantitative structure‐property relationships. In: Gasteiger J (ed) Handbook of chemoinformatics, vol 3. Wiley, Weinheim, pp 1314–1335Google Scholar
  74. 74.
    Kier LB, Hall LH (1976) Molecular connectivity in chemistry and drug research. Academic Press, New YorkGoogle Scholar
  75. 75.
    Kier LB, Hall LH (1986) Molecular connectivity in structure‐activity analysis. Research Studies Press, LetchworthGoogle Scholar
  76. 76.
    Kier LB, Hall LH (1999) Molecular structure description. The electrotopological state. Academic Press, San DiegoGoogle Scholar
  77. 77.
    Klon AE, Diller DJ (2007) Library fingerprints: a novel approach to the screening of virtual libraries. J Chem Inf Model 47:1354–1365Google Scholar
  78. 78.
    Klon AE, Glick M, Davies JW (2004) Combination of a naive Bayes classifier with consensus scoring improves enrichment of high‐throughput docking results. J Med Chem 47:4356–4359Google Scholar
  79. 79.
    Klon AE, Glick M, Thoma M, Acklin P, Davies JW (2004) Finding more needles in the haystack: a simple and efficient method for improving high‐throughput docking results. J Med Chem 47:2743–2749Google Scholar
  80. 80.
    Klon AE, Lowrie JF, Diller DJ (2006) Improved naïve Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J Chem Inf Model 46:1945–1956Google Scholar
  81. 81.
    Kohavi R (1995) The power of decision tables. In: Lavrac N, Wrobel S (eds) ECML-95 8th european conference on machine learning. Lecture Notes in Computer Science, vol 912. Springer, Heraclion, pp 174–189Google Scholar
  82. 82.
    Kohavi R (1996) Scaling up the accuracy of naive-Bayes classifiers: a decision‐tree hybrid. In: Simoudis E, Han J, Fayyad UM (eds) Proc of the 2nd international conference on knowledge discovery and data mining (KDD-96). AAAI Press, Menlo Park, pp 202–207Google Scholar
  83. 83.
    Kononenko I, Kukar M (2007) Machine learning and data mining: introduction to principles and algorithms. Horwood, Westergate, p 454Google Scholar
  84. 84.
    Konovalov DA, Coomans D, Deconinck E, Vander Heyden Y (2007) Benchmarking of QSAR models for blood‐brain barrier permeation. J Chem Inf Model 47:1648–1656Google Scholar
  85. 85.
    Kumar R, Kulkarni A, Jayaraman VK, Kulkarni BD (2004) Structure‐activity relationships using locally linear embedding assisted by support vector and lazy learning regressors. Internet Electron J Mol Des 3:118–133Google Scholar
  86. 86.
    le Cessie S, van Houwelingen JC (1992) Ridge estimators in logistic regression. Appl Statist 41:191–201zbMATHGoogle Scholar
  87. 87.
    Leong MK (2007) A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability. Chem Res Toxicol 20:217–226Google Scholar
  88. 88.
    Lepp Z, Kinoshita T, Chuman H (2006) Screening for new antidepressant leads of multiple activities by support vector machines. J Chem Inf Model 46:158–167Google Scholar
  89. 89.
    LiH, Yap CW, Ung CY, Xue Y, Cao ZW, Chen YZ (2005) Effect of selectionof molecular descriptors on the prediction of blood‐brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45:1376–1384Google Scholar
  90. 90.
    Li S, Fedorowicz A, Singh H, Soderholm SC (2005) Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Model 45:952–964Google Scholar
  91. 91.
    Li W-X, Li L, Eksterowicz J, Ling XB, Cardozo M (2007) Significance analysis and multiple pharmacophore models for differentiating P‑glycoprotein substrates. J Chem Inf Model 47:2429–2438Google Scholar
  92. 92.
    Liao Q, Yao J, Yuan S (2007) Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines. Mol Divers 11:59–72Google Scholar
  93. 93.
    Mangasarian OL, Musicant DR (2000) Robust linear and support vector regression. IEEE Trans Pattern Anal Mach Intell 22:950–955Google Scholar
  94. 94.
    Mangasarian OL, Musicant DR (2002) Large scale kernel regression via linear programming. Mach Learn 46:255–269zbMATHGoogle Scholar
  95. 95.
    Merkwirth C, Mauser HA, Schulz-Gasch T, Roche O, Stahl M, Lengauer T (2004) Ensemble methods for classification in cheminformatics. J Chem Inf Comput Sci 44:1971–1978Google Scholar
  96. 96.
    Mitchell TM (1997) Machine learning. McGraw-Hill, Maidenhead, p 432zbMATHGoogle Scholar
  97. 97.
    Müller K-R, Rätsch G, Sonnenburg S, Mika S, Grimm M, Heinrich N (2005) Classifying ‘drug‐likeness’ with kernel‐based learning methods. J Chem Inf Model 45:249–253Google Scholar
  98. 98.
    Neugebauer A, Hartmann RW, Klein CD (2007) Prediction of protein‐protein interaction inhibitors by chemoinformatics and machine learning methods. J Med Chem 50:4665–4668Google Scholar
  99. 99.
    Neumann D, Kohlbacher O, Merkwirth C, Lengauer T (2006) A fully computational model for predicting percutaneous drug absorption. J Chem Inf Model 46:424–429Google Scholar
  100. 100.
    Nidhi, Glick M, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple‐category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46:1124–1133Google Scholar
  101. 101.
    Nigsch F, Bender A, van Buuren B, Tissen J, Nigsch E, Mitchell JBO (2006) Melting point prediction employing k‑nearest neighbor algorithms and genetic parameter optimization. J Chem Inf Model 46:2412–2422Google Scholar
  102. 102.
    Oloff S, Muegge I (2007) kScore: a novel machine learning approach that is not dependent on the data structure of the training set. J Comput-Aided Mol Des 21:87–95ADSGoogle Scholar
  103. 103.
    Oloff S, Zhang S, Sukumar N, Breneman C, Tropsha A (2006) Chemometric analysis of ligand receptor complementarity: Identifying complementary ligands based on receptor information (CoLiBRI). J Chem Inf Model 46:844–851Google Scholar
  104. 104.
    Palmer DS, O'Boyle NM, Glen RC, Mitchell JBO (2007) Random forest models to predict aqueous solubility. J Chem Inf Model 47:150–158Google Scholar
  105. 105.
    Pelletier DJ, Gehlhaar D, Tilloy-Ellul A, Johnson TO, Greene N (2007) Evaluation of a published in silico model and construction of a novel Bayesian model for predicting phospholipidosis inducing potential. J Chem Inf Model 47:1196–1205Google Scholar
  106. 106.
    Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods – support vector learning. MIT Press, Cambridge, pp 185–208Google Scholar
  107. 107.
    Plewczynski D, Spieser SAH, Koch U (2006) Assessing different classification methods for virtual screening. J Chem Inf Model 46:1098–1106Google Scholar
  108. 108.
    Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann, San MateoGoogle Scholar
  109. 109.
    Ren S (2002) Classifying class I and class II compounds by hydrophobicity and hydrogen bonding descriptors. Environ Toxicol 17:415–423Google Scholar
  110. 110.
    Ripley BD (2008) Pattern recognition and neural networks. Cambridge University Press, Cambridge, p 416zbMATHGoogle Scholar
  111. 111.
    Rodgers S, Glen RC, Bender A (2006) Characterizing bitterness: identification of key structural features and development of a classification model. J Chem Inf Model 46:569–576Google Scholar
  112. 112.
    Rusinko A, Farmen MW, Lambert CG, Brown PL, Young SS (1999) Analysis of a large structure/biological activity data set using recursive partitioning. J Chem Inf Comput Sci 39:1017–1026Google Scholar
  113. 113.
    Sakiyama Y, Yuki H, Moriya T, Hattori K, Suzuki M, Shimada K, Honma T (2008) Predicting human liver microsomal stability with machine learning techniques. J Mol Graph Modell 26:907–915Google Scholar
  114. 114.
    Schneider N, Jäckels C, Andres C, Hutter MC (2008) Gradual in silico filtering for druglike substances. J Chem Inf Model 48:613–628Google Scholar
  115. 115.
    Schölkopf B, Smola AJ (2002) Learning with kernels. MIT Press, CambridgeGoogle Scholar
  116. 116.
    Schölkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765Google Scholar
  117. 117.
    Schölkopf B, Burges CJC, Smola AJ (1999) Advances in kernel methods: support vector learning. MIT Press, CambridgeGoogle Scholar
  118. 118.
    Schroeter TS, Schwaighofer A, Mika S, ter Laak A, Suelzle D, Ganzer U, Heinrich N, Müller K-R (2007) Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J Comput-Aided Mol Des 21:485–498Google Scholar
  119. 119.
    Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, CambridgeGoogle Scholar
  120. 120.
    ShenM, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A(2002) Quantitative structure‐activity relationship analysis offunctionalized amino acid anticonvulsant agents using k‑nearest neighbor and simulated annealing PLS methods. J Med Chem 45:2811–2823Google Scholar
  121. 121.
    Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A (2003) Development and validation of k‑nearest‐neighbor QSPR models of metabolic stability of drug candidates. J Med Chem 46:3013–3020Google Scholar
  122. 122.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222Google Scholar
  123. 123.
    Sommer S, Kramer S (2007) Three data mining techniques to improve lazy structure‐activity relationships for noncongeneric compounds. J Chem Inf Model 47:2035–2043Google Scholar
  124. 124.
    Sorich MJ, McKinnon RA, Miners JO, Smith PA (2006) The importance of local chemical structure for chemical metabolism by human uridine 5'‑diphosphate‐glucuronosyltransferase. J Chem Inf Model 46:2692–2697Google Scholar
  125. 125.
    Sun H (2005) A naive Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J Med Chem 48:4031–4039Google Scholar
  126. 126.
    Suykens JAK (2001) Support vector machines: a nonlinear modelling and control perspective. Eur J Control 7:311–327Google Scholar
  127. 127.
    Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific, SingaporezbMATHGoogle Scholar
  128. 128.
    Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958Google Scholar
  129. 129.
    Svetnik V, Wang T, Tong C, A Liaw, Sheridan RP, Song Q (2005) Boosting: an ensemble learning tool for compound classification and QSAR modeling. J Chem Inf Model 45:786–799Google Scholar
  130. 130.
    Swamidass SJ, Chen J, Phung P, Ralaivola L, Baldi P (2005) Kernels for small molecules and the prediction of mutagenicity, toxicity and anti‐cancer activity. Bioinformatics 21[S1]:i359–i368Google Scholar
  131. 131.
    Terfloth L, Bienfait B, Gasteiger J (2007) Ligand‐based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substrates. J Chem Inf Model 47:1688–1701Google Scholar
  132. 132.
    Tobita M, Nishikawa T, Nagashima R (2005) A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorg Med Chem Lett 15:2886–2890Google Scholar
  133. 133.
    Todeschini R, Consonni V (2003) Descriptors from molecular geometry. In: Gasteiger J (ed) Handbook of chemoinformatics, vol 3. Wiley, Weinheim, pp 1004–1033Google Scholar
  134. 134.
    Tong W, Hong H, Fang H, Xie Q, Perkins R (2003) Decision forest: Combining the predictions of multiple independent decision tree models. J Chem Inf Comput Sci 43:525–531Google Scholar
  135. 135.
    Tong W, Xie Q, Hong H, Shi L, Fang H, Perkins R (2004) Assessment of prediction confidence and domain extrapolation of two structure‐activity relationship models for predicting estrogen receptor binding activity. Env Health Perspect 112:1249–1254Google Scholar
  136. 136.
    Trinajstić N (1992) Chemical graph theory. CRC Press, Boca RatonGoogle Scholar
  137. 137.
    Urrestarazu Ramos E, Vaes WHJ, Verhaar HJM, Hermens JLM (1998) Quantitative structure‐activity relationships for the aquatic toxicity of polar and nonpolar narcotic pollutants. J Chem Inf Comput Sci 38:845–852Google Scholar
  138. 138.
    Vapnik VN (1979) Estimation of dependencies based on empirical data. Nauka, MoscowGoogle Scholar
  139. 139.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkzbMATHGoogle Scholar
  140. 140.
    Vapnik VN (1998) Statistical learning theory. Wiley, New YorkzbMATHGoogle Scholar
  141. 141.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999Google Scholar
  142. 142.
    Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Comput 12:2013–2036Google Scholar
  143. 143.
    Vapnik VN, Chervonenkis AY (1974) Theory of pattern recognition. Nauka, MoscowzbMATHGoogle Scholar
  144. 144.
    Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Automat Remote Control 24:774–780Google Scholar
  145. 145.
    Varnek A, Kireeva N, Tetko IV, Baskin II, Solov'ev VP (2007) Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points? J Chem Inf Model 47:1111–1122Google Scholar
  146. 146.
    Vogt M, Bajorath J (2008) Bayesian similarity searching in high‐dimensional descriptor spaces combined with Kullback–Leibler descriptor divergence analysis. J Chem Inf Model 48:247–255Google Scholar
  147. 147.
    von Korff M, Sander T (2006) Toxicity‐indicating structural patterns. J Chem Inf Model 46:536–544Google Scholar
  148. 148.
    Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A (2006) QSAR modeling of human serum protein binding with several modeling techniques utilizing structure‐information representation. J Med Chem 49:7169–7181Google Scholar
  149. 149.
    Wang J, Du H, Yao X, Hu Z (2007) Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid‐search support vector machine. Anal Chim Acta 601:156–163Google Scholar
  150. 150.
    Watson P (2008) Naïve Bayes classification using 2D pharmacophore feature triplet vectors. J Chem Inf Model 48:166–178Google Scholar
  151. 151.
    Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco, p 525Google Scholar
  152. 152.
    Xiao Z, Xiao Y-D, Feng J, Golbraikh A, Tropsha A, Lee K-H (2002) Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k‑nearest neighbor QSAR method. J Med Chem 45:2294–2309Google Scholar
  153. 153.
    Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ (2004) Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 44:1630–1638Google Scholar
  154. 154.
    Yamashita F, Hara H, Ito T, Hashida M (2008) Novel hierarchical classification and visualization method for multiobjective optimization of drug properties: application to structure‐activity relationship analysis of cytochrome P450 metabolism. J Chem Inf Model 48:364–369Google Scholar
  155. 155.
    Yap CW, Chen YZ (2005) Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J Chem Inf Model 45:982–992Google Scholar
  156. 156.
    Yap CW, Cai CZ, Xue Y, Chen YZ (2004) Prediction of torsade‐causing potential of drugs by support vector machine approach. Toxicol Sci 79:170–177Google Scholar
  157. 157.
    Yu G-X, Park B-H, Chandramohan P, Munavalli R, Geist A, Samatova NF (2005) In silico discovery of enzyme‐substrate specificity‐determining residue clusters. J Mol Biol 352:1105–1117Google Scholar
  158. 158.
    Yue P, Li Z, Moult J (2005) Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol 353:459–473Google Scholar
  159. 159.
    Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A (2006) A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model 46:1984–1995Google Scholar
  160. 160.
    Zhang S, Golbraikh A, Tropsha A (2006) Development of quantitative structure‐binding affinity relationship models based on novel geometrical chemical descriptors of the protein‐ligand interfaces. J Med Chem 49:2713–2724Google Scholar
  161. 161.
    Zheng WF, Tropsha A (2000) Novel variable selection quantitative structure‐property relationship approach based on the k‑nearest‐neighbor principle. J Chem Inf Comput Sci 40:185–194Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ovidiu Ivanciuc
    • 1
  1. 1.Department of Biochemistry and Molecular BiologyUniversity of Texas, Medical BranchGalvestonUSA