Applications of machine learning and computational intelligence to drug discovery and development

Drug Development Research

Volumn 72, Issue 1, 2011, Pages 53-65

  Hecht, David ^a  

^a Southwestern College   (United States)

Author keywords

ADME tox; Artificial neural networks; Computational intelligence; Evolutionary algorithms; Fuzzy logic; High throughput screening; Machine learning; QSAR; QSPR; Support vector machines; Virtual screening

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; COMPUTER MODEL; DRUG ABSORPTION; DRUG DEVELOPMENT; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG RESEARCH; DRUG STRUCTURE; EVOLUTIONARY ALGORITHM; FUZZY LOGIC; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PHYSICAL CHEMISTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; REVIEW; SUPPORT VECTOR MACHINE;

EID: 79951792796     PISSN: 02724391     EISSN: 10982299     Source Type: Journal    
DOI: 10.1002/ddr.20402     Document Type: Review

References (190)

1. Agatonovic-Kustrin S, Beresford R, Yusof APM. 2001. ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors. J Pharmaceut Biomed 26:241.
2. Agatonovic-Kustrin S, Ling LH, Tham SY, Alany RG. 2002. Molecular descriptors that influence the amount of drugs transfer into human breast milk. J Pharmaceut Biomed Anal 29:103-119. (Pubitemid 34621274)
3. Ajay, Bemis GW, Murcko MA. 1999. Designing libraries with CNS activity. J Med Chem 42:4942-4951.
4. Andricopulo AD, Salum LB, Abraham DJ. 2009. Structure-based drug design strategies in medicinal chemistry. Curr Top Med Chem 9:771-790.
5. Arimoto R, Prasad MA, Gifford EM. 2005. Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors. J Biomol Screen 10: 197-205. (Pubitemid 40617157)
6. Balakin KV, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Ekins S. 2005. Comprehensive computational assessment of ADME properties using mapping techniques. Curr Drug Discov Technol 2:99-113. (Pubitemid 40943980)
7. Bazeley PS, Prithivi S, Struble CA, Povinelli RJ, Sem DS. 2006. Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling. J Chem Inf Model 46:2698-2708. (Pubitemid 46008139)
8. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and regression trees. London: Chapman & Hall/CRC.
9. Bremmerman HJ. 1962. Optimization through evolution and recombination. In: Yovits MC, Jacobi GT, Goldstein GD, editors. Self-organizing systems. Washington, DC: Spartan Press.
10. Bruneau P. 2001. Search for predictive generic model of aqueous solubility using Bayesian neural nets. J Chem Inf Comput Sci 41: 1605-1616.
11. Burton J, Ijjaali I, Petitet F, Michel A, Vercauteren DP. 2009. Virtual screening for cytochromes P450: successes of machine learning filters. Combin Chem High Throughput Screen 12:369-382.
12. Chekmarev DS, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ. 2008. Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem Res Toxicol 21:1304-1314. (Pubitemid 351967396)
13. Center for Drug Evaluation and Research (CDER). 2010. Report: http://www.fda.gov/cder/rdmt.
14. Cheng A, Merz Jr KM. 2003. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem 46:3572-3580. (Pubitemid 36988646)
15. Cheung M, Johnson S, Hecht D, Fogel GB. 2008 Quantitative structure-property relationships for drug solubility prediction using evolved neural networks. 2008 IEEE Congress on Evolutionary Computation, Hong Kong. p 698-693.
16. Clark RD. 2009. Prospective ligand- and target-based 3D QSAR: state of the art 2008. Curr Top Med Chem 9:791-810.
17. Coe KJ, Nelson SD, Ulrich RG, He Y, Dai X, Cheng O, Caguyong M, Roberts CJ, Slatter JG. 2006. Profiling the hepatic effects of flutamide in rats: a microarray comparison with classical aryl hydrocarbon receptor ligands and atypical CYP1A inducers. Drug Metab Dispos 34:1266-1275.
18. Collings FB, Vaidya VS. 2008. Novel technologies for the discovery and quantitation of biomarkers of toxicity. Toxicology 245:167-174.
19. Darvas F, Keserü G, Papp Á, Dormán G, Ürge L, Krajcsi P. 2002. In silico and ex silico ADME approaches for drug discovery. Curr Topics Med Chem 2:1287-1304.
20. Deconinck E, Hancock T, Coomans D, Massart DL, Vander Heyden Y. 2005. Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. J Pharmaceut Biomed 39:91.
21. Deǧim Z. 2005. Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis. Drug Dev Indust Pharm 31:935-942. (Pubitemid 41540946)
22. Devillers J. 1996. Neural Networks in QSAR and Drug Design an essential reference source for those on the frontiers of this field. New York: Academic Press.
23. Di Fenza A, Alagona G, Ghio C, Leonardi R, Giolitti A, Madami A. 2007. Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach. J Comput Aided Mol Des 21:207-221. (Pubitemid 46510844)
24. DiMasi JA, Grabowski HG. 2007. The cost of biopharmaceutical R&D: is biotech different? Manager Decis Econ 28:469-479.
25. Dong X, Bondugula R, Popescu, M. 2006. Bioinformatics and fuzzy logic. 2006 IEEE Int Conf Fuzzy Syst 817-824.
26. Doniger S, Hofmann T, Yeh J. 2002. Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms. J Comput Biol 9:849-864. (Pubitemid 36207918)
27. Dorigo M, Gambardella LM. 1997. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53-66. (Pubitemid 127771018)
28. Dorronsoro I, Chana A, Abasolo I, Castro A, Gil C, Stud M, Martinez A. 2004. CODES/Neural Network Model: a useful tool for in silico prediction of oral absorption and blood-brain barrier permeability of structurally diverse drugs. Qsar Comb Sci 23:89.
29. Duch W, Swaminathan K, Meller J. 2007. Artificial intelligence approaches for rational drug design and discovery. Curr Pharmaceut Des 13:1497-1508. (Pubitemid 46863156)
30. Duda RO, Hart PE, Stork DG. 2001. Pattern classification, 2nd ed. New York: John Wiley & Sons.
31. Eberhart RC, Shi Y, Kennedy J. 2001. Swarm intelligence. San Francisco, CA: Morgan Kaufmann.
32. Ekins S. 2006. Systems-ADME/Tox: resources and network approaches. J Pharmacol Toxicol Methods 53:38-66. (Pubitemid 43054914)
33. Ekins S, Ring BJ, Grace J, McRobie-Belle DJ, Wrighton SA. 2000. Present and future in vitro approaches for drug metabolism. J Pharmacol Toxicol Methods 44:313-324. (Pubitemid 32239483)
34. Engelbrecht, AP. 2003. Computational intelligence: an introduction. New York: John Wiley & Sons.
35. Engkvist O, Wrede P. 2002. High-throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors. J Chem Inf Comput Sci 42:1247-1249. (Pubitemid 35360309)
36. Fogel DB. 2006. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 3rd ed. New York: IEEE Press.
37. Fogel DB, Fogel LJ, Porto VW. 1990. Evolving neural networks. Biol Cybern 63:487-493. (Pubitemid 20331189)
38. Fogel GB. 2008. Computational intelligence approaches for pattern discovery in biological systems. Brief Bioinformat 9:307-316. (Pubitemid 351890827)
39. Fogel GB, Cheung M. 2005. Derivation of quantitative structure-toxicity relationships for ecotoxicological effects of organic chemicals: evolving neural networks and evolving rules. 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK. p 274-281.
40. Fogel GB, Tran J, Johnson S, Hecht D. 2010. Machine learning approaches for customized docking scores: modeling of inhibition of mycobacterium tuberculosis enoyl acyl carrier protein reductase. 2010 IEEE Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 art. no. 5510700; pp 243-248.
41. Fogel LJ, Owens A, Walsh MJ. 1966. Artificial intelligence through simulated evolution. New York, NY: John Wiley & Sons.
42. Fujiwara S, Yamashita F, Hashida M. 2002. Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network. Int J Pharm 237:95-105. (Pubitemid 34310483)
43. Ganter B, Zidek N, Hewitt PR, Müller D, Vladimirova A. 2008 Pathway analysis tools and toxicogenomics reference databases for risk assessment. Pharmacogenomics 9:35-54. (Pubitemid 351560530)
44. Ghafourian T, Barzegar-Jalali M, Dastmalchi S, Khavari-Khorasani T, Hakimiha N, Nokhodchi A. 2006. QSPR models for the prediction of apparent volume of distribution. Int J Pharmaceut 319:82-97. (Pubitemid 44051808)
45. Ghuloum AM, Sage CR, Jain AN. 1999. Molecular hashkeys: a novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules. J Med Chem 42:1739.
46. Gomase VS, Parundekar AN. 2009. microRNA: human disease and development. Int J Bioinform Res Appl 5:479-500.
47. Gomase VS, Tagore S. 2008. Toxicogenomics. Curr Drug Metab 9: 250-254. (Pubitemid 351843593)
48. Gomase VS, Tagore S, Changbhale SS, Kale KV. 2008a. Pharmacogenomics. Curr Drug Metab 9:207-212. (Pubitemid 351843587)
49. Gomase VS, Tagore S, Kale KV. 2008b. Microarray: an approach for current drug targets. Curr Drug Metab 9:221-231. (Pubitemid 351843588)
50. Gozalbes R, Doucet JP, Derouin F. 2002. Application of topological descriptors in QSAR and drug design: history and new trends. Curr Drug Targets Infect Disord 2:93-102. (Pubitemid 34649859)
51. Guangli M, Yiyu C. 2006. Predicting Caco-2 permeability using support vector machine and chemistry development kit. J Pharm Pharm Sci 9:210-221.
52. Gunturi SB, Narayanan R, Khandelwal A. 2006. In silico ADME modelling: computational models to predict human serum albumin binding using ant colony systems. Bioorgan Med Chem 14:4118-4129.
53. Halperin I, Ma B, Wolfson H, Nussinov R. 2002. Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47:409-443. (Pubitemid 34614722)
54. Han LY, Ma XH, Lin HH, Jia J, Zhu F, Xue Y, Li ZR, Cao ZW, Ji ZL, Chen YZ. 2008a. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J Mol Graph Model 26: 1276-1286.
55. Han L, Wang Y, Bryant SH. 2008b. Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem. BMC Bioinformatics 9:401.
56. Hastie T, Tibshirani R, Friedman J. 2001. The elements of statistical learning. New York: Springer.
57. Haykin S. 1998. Neural networks: a comprehensive foundation. Upper Saddle River, NJ: Prentice-Hall.
58. Hecht D, Fogel GB. 2007 Evolved neural networks for high throughput ligand screening. IEEE/ACM Transact Comput Biol Bioinform 4:476-484. (Pubitemid 47274723)
59. Hecht D, Fogel G. 2009a. Computational intelligence methods for docking scores. Curr Comput Assist Drug Des 5:56-68.
60. Hecht D, Fogel G. 2009b. Computational Intelligence Methods for ADMET Prediction. In: Caldwell GW, Atta-ur-Rahman Yan Z, Choudhary MI, editors. Frontiers in drug discovery and development, Vol. 4. Bentham Science Publishers. p 351-377.
61. Hecht D, Cheung M, Fogel GB. 2008. QSAR using evolved neural networks for the inhibition of mutant PfDHFR by pyrimethamine derivatives. Biosystems 92:10-15.
62. Hecht D, Cheung M, Fogel GB. 2009. Docking scores and QSAR using evolved neural networks for the pan-inhibition of wild-type and mutant PfDHFR by cycloguanil derivatives. 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway. p 262-269.
63. Holland JH. 1975. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
64. Hou TJ, Xu XJ. 2002. ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs. J Mol Model 8:337.
65. Hou T, Wang J, Zhang W, Wang W, Xu X. 2006. Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem 13:2653-2667. (Pubitemid 44364143)
66. Hou T, Wang J, Li Y. 2007. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. J Chem Inf Model 47:2408-2415. (Pubitemid 350275105)
67. 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.
68. Huuskonen J, Salo M, Taskinen J. 1998. J. Aqueous solubility prediction of drugs based on molecular topology and neural network modeling. J Chem Inf Comput Sci 38:450-456. (Pubitemid 128594458)
69. Huuskonen J, Rantanen J, Livingstone D. 2000. Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices. Eur J Med Chem 35:1081.
70. Jensen BF, Vind C, Padkjær SB, Brockhoff PB, Refsgaard HHF. 2007. In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using Gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. J Med Chem 50: 501-511.
71. Jia L, Sun H. 2008. Support vector machines classification of hERG liabilities based on atom types. Bioorgan Med Chem 16: 6252-6260. (Pubitemid 351758818)
72. Judson R, Elloumi F, Stzer RW, Li Z, Shah I. 2008. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformat 9: 241-257.
73. Kennedy T. 1997 Managing the drug discovery/development interface. Drug Discov Today 2:436-444. (Pubitemid 27387353)
74. Khedkar SA, Malde AK, Coutinho EC. 2008. In silico screening of ligand databases: methods and applications. Ind J Pharmaceut Sci 68:689-696.
75. Kitchen DB, Decornez H, Furr JR, Bajorath J. 2004. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Rev Drug Discov 3:935-949. (Pubitemid 39529931)
76. Klein CDP, Hopfinger AJ. 1998. Pharmacological activity and membrane interactions of antiarrhythmics: 4D-QSAR/QSPR analysis. Pharmaceut Res 15:303-311. (Pubitemid 28144245)
77. Klon AE, Glick M, Davies JW. 2004a. Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. J Med Chem 47:4356-4359. (Pubitemid 39096825)
78. Klon AE, Glick M, Thoma M, Acklin P, Davies JW. 2004b. Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results. J Med Chem 47: 2743-2749. (Pubitemid 38656727)
79. Konar A. 2005. Computational intelligence; principles, techniques and applications. Berlin: Springer.
80. Kortagere S, Chekmarev D, Welsh WJ, Ekins S. 2008. New predictive models for blood-brain barrier permeability of drug-like molecules. Pharmaceut Res 25:1836-1845.
81. Koza J. 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
82. Kriegl JM, Arnhold T, Beck B, Fox T. 2005a. Prediction of human cytochrome P450 inhibition using support vector machines. QSAR Comb Sci 24:491-502. (Pubitemid 40839677)
83. Kriegl JM, Arnhold T, Beck B, Fox T. 2005b. A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J Comput Aided Mol Des 19:189-201. (Pubitemid 40974006)
84. Krovat EM, Steindl T, Langer T. 2005. Recent advances in docking and scoring. Curr Comput Aided Drug Des 1:93-102.
85. Kulkarni AJ, Jayaraman VK, Kulkarni BD. 2009. Review on lazy learning regressors and their applications in QSAR. Combinator Chem High Throughput Screen 12:440-450.
86. Kumar RA, Clark DS. 2006. High-throughput screening of biocatalytic activity: applications in drug discovery. Curr Opin Chem Biol 10:162-168.
87. Lahoz A, Gombau L, Donato MT, Castell JV, Gómez-Lechón MJ. 2006. In vitro ADME medium/high-throughput screening in drug preclinical development. Mini Rev Med Chem 6:1053-1062.
88. Landavazo DG, Fogel GB, Fogel DB. 2002 Quantitative structure-activity relationships by evolved neural networks for the inhibition of dihydrofolate reductase by pyrimidines. BioSystems 65: 37-47. (Pubitemid 34218938)
89. Leong MK. 2007. A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability. Chem Res Toxicol 20:217-226. (Pubitemid 46408858)
90. Lessigiarska I, Pajeva I, Cronin MT, Worth AP. 2005. 3D QSAR investigation of the blood-brain barrier penetration of chemical compounds. SAR QSAR Environ Res 16:79-91. (Pubitemid 40623244)
91. Lewis RA. 2005. A general method for exploiting QSAR models in lead optimization. J Med Chem 48:1638-1648. (Pubitemid 40364570)
92. Li H, Yap CW, Ung CY, Xue Y, Cao ZW, Chen YZ. 2005. Effect of selection of molecular descriptors on the prediction of bloodbrain barrier penetrating and non-penetrating agents by statistical learning methods. J Chem Inf Model 45:1376.
93. Li H, Yap CW, Ung CY, Xue Y, Li ZR, Han LY, Lin HH, Chen YZ. 2007. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci 96:2838-2860. (Pubitemid 350221187)
94. Li Q, Jørgensen FS, Oprea T, Brunak S, Taboureau O. 2008. hERG classification model based on a combination of support vector machine method and GRIND descriptors. Mol Pharmaceut 5: 117-127. (Pubitemid 351300041)
95. Liu HX, Hu RJ, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT. 2005. The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine. J Comput Aid Mol Des 19:33.
96. Liu P, Long W. 2009. Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 10:1978-1998.
97. Liu R, So S-S. 2001. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility. J Chem Inf Comput Sci 41: 1633-1639.
98. Liu RF, Sun HM, So SS. 2001. Development of quantitative structure: property relationship models for early ADME evaluation in drug discovery. 2. Blood-brain barrier penetration. J Chem Inf Comp Sci 41:1623.
99. Lombardo F, Gifford E, Shalavea MY. 2003. In silico ADME prediction: data, models, facts and myths. Mini Rev Med Chem 3: 861-875.
100. Lüpfort C, Reichel A. 2005. Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery. Chem Biodivers 2:1462-1486. (Pubitemid 41762899)
101. Ma CYC, Wong SWM, Hecht D, Fogel GB. 2006 Evolved neural networks for high throughput anti-HIV ligand screening. 2006 IEEE Congress on Evolutionary Computation, Vancouver, Canada. p 9284-9291.
102. Ma XH, Jia J, Zhu F, Xue Y, Li ZR, Chen YZ. 2009. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Combinator Chem High Throughput Screen 12:344-357.
103. Maggioli J, Hoover A, Weng L. 2006. Toxicogenomic analysis methods for predictive toxicology. J Pharmacol Toxicol Methods 53:31-37. (Pubitemid 43052701)
104. Mahé P, Vert J-P. 2009. Virtual screening with support vector machines and structure kernels. Combinator Chem High Throughput Screen 12:409-423.
105. Manallack DT, Livingstone DJ. 1999. Neural networks in drug discovery, have they lived up to their promise? Eur J Med Chem 34:195-208.
106. Manallack DT, Tehan BG, Gancia E, Hudson BD, Ford MG, Livingstone DJ, Whitley DC, Pitt WR. 2003. A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J Chem Inf Comput Sci 43:674-679.
107. Maréchal E. 2008. Chemogenomics: a discipline at the crossroad of high throughput technologies, biomarker research, combinatorial chemistry, genomics, cheminformatics, bioinformatics and artificial intelligence. Combinator Chem High Throughput Screen 11: 583-586.
108. Martin O, Schomburg D. 2008. Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines. Proteins 70:1367-1378. (Pubitemid 351304096)
109. Mazzatorta P, Tran L-A, Schilter B, Grigorov M. 2007. Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of Ames test mutagenicity. J Chem Inf Model 47:34-38. (Pubitemid 46225557)
110. McCulloch WS, Pitts W. 1943. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115-133.
111. McElroy NR, Jurs PC. 2001. Prediction of aqueous solubility of heteroatom-containing organic compounds from molecular structure. J Chem Inf Comput Sci 41:1237.
112. Melville JL, Burke EK, Hirst JD. 2009. Machine learning in virtual screening. Combinator Chem High Throughput Screen 12:332-343.
113. Mendel JM. 2001. Uncertain rule-based fuzzy logic systems: introduction and new directions. Upper-Saddle River, NJ: Prentice-Hall.
114. Mendel JM. 2007. Type-2 fuzzy sets and systems: an overview. IEEE Comput Intell Mag 2:20-29.
115. Mendrick DL. 2008. Genomic and genetic biomarkers of toxicity. Toxicology 245:175-181.
116. Michalewicz Z. 1996. Genetic algorithms+data structures= evolution programs, 3rd ed. Berlin: Springer.
117. Mitchell BE, Jurs PC. 1998. Prediction of aqueous solubility of organic compounds from molecular structure. J Chem Inf Comput Sci 38:489.
118. Mohan CG, Gandhi T, Garg D, Shinde R. 2007. Computer assisted methods in chemical toxicity prediction. Mini Rev Med Chem 7: 499-507. (Pubitemid 46845797)
119. Moon T, Chi MH, Kim DH, Yoon CN, Choi YS. 2000. Quantitative structure-activity relationships (QSAR) study of flavinoid derivatives for inhibition of cytochrome P450 1A2. QSAR 19:257-263. (Pubitemid 30428231)
120. Mordeson JN, Malik DS, Cheng S-C. 2000. Fuzzy mathematics in medicine. Secaucus, NJ: Springer-Verlag.
121. Nestorov I, Gueorguieva I, Jones HM, Houston B, Rowland M. 2002. Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. Drug Metab Dispos 30:276-282. (Pubitemid 34175018)
122. Nicolotti O, Carotti A. 2006. QSAR and QSPR studies of a highly structured physicochemical domain. J Chem Inf Model 46: 264-276. (Pubitemid 43282121)
123. Niwa T. 2003. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures. J Chem Inf Comp Sci 43:113-119.
124. Palmer DS, O'Boyle NM, Glen RC, Mitchell JB. 2007. Random forest models to predict aqueous solubility. J Chem Inf Model 47: 150-158. (Pubitemid 46225571)
125. PhRMA Industry Profile. 2010 Report, http://www.phrma.org/publications.
126. Plewczynski D, Spieser SAS, Koch U. 2006. Assessing different classification methods for virtual screening. J Chem Inf Model 46: 1098-1106. (Pubitemid 43999155)
127. Plewczynski D, Spieser AH, Koch U. 2009. Performance of machine learning methods for ligand-based virtual screening. Combinator Chem High Throughput Screen 12:358-368.
128. Porto VW, Fogel DB, Fogel LJ, Fogel GB, Johnson N, Cheung M. 2005. Classifying sonor returns for the presence of mines: evolving neural networks and evolving rules. In: Fogel DB, Piuri V, editors. 2005 IEEE Symposium on Computational Intelligence for Homeland Security and Personal Safety. Piscataway, NJ: IEEE Press. p 123-130.
129. Prentis RA, Lis Y, Walker Br SR. 1988. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). J Clin Pharmacol 25:387-396.
130. Rechenberg I. 1973. Evolutions strategie: optimerung technischer Systeme nach Prinzipien der biologischen Evolution. [Evolutionary strategy: Technical optimization of systems based on the principles of biological evolution] Stuttgart, Germany: Fromman-Holzboog.
131. Rosenblatt F. 1962. Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Washington, DC: Spartan Books.
132. Ruspini EH, Bonissone PP, Pedrycz W, editors. 1998. Handbook of fuzzy computation. Bristol, UK: Oxford University Press.
133. Saiakhov RD, Stefan LR, Klopman G. 2000. Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs. Persp Drug Disc Des 19: 133-135.
134. 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 Model 26:907-915. (Pubitemid 350298011)
135. Santos-Filho OA, Hopfinger AJ, Cherkasov A, de Alencastro RB. 2009. The receptor-dependent QSAR paradigm: an overview of the current state of the art. Med Chem 5:359-366.
136. Schuster D, Laggner C, Langer T. 2005. Why drugs fail: a study on side effects in new chemical entities. Curr Pharm Des 11: 3545-3559. (Pubitemid 41300759)
137. 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: 3013-3020. (Pubitemid 36775924)
138. Singh SS. 2006. Preclinical pharmacokinetics: an approach towards safer and efficacious drugs. Current Drug Metab 7:165-182.
139. Slatter JG, Cheng O, Cornwell PD, de Souza A, Rockett J, Rushmore T, Hartley D, Evers R, He Y, Dai X, Hu R, Caguyong M, Roberts CJ, Castle J, Ulrich RG. 2006a. Microarray-based compendium of hepatic gene expression profiles for prototypical ADME gene-inducing compounds in rats and mice in vivo. Xenobiotica 36:902-937. (Pubitemid 44844108)
140. Slatter JG, Templeton IE, Castle JC, Kulkarni A, Rushmore TH, Richards K, He Y, Dai X, Cheng OJ, Caguyong M, Ulrich RG. 2006b. Compendium of gene expression profiles comprising a baseline model of the human liver drug metabolism transcriptome. Xenobiotica 36:938-962. (Pubitemid 44844109)
141. Storn R, Price K. 1995. Differential evolution: a simple and efficient adaptive scheme for globaloptimization over continuous spaces. Technical Report TR-95-012, Berkeley, CA: ICSI, March 1995.
142. Storn R. 1999. System design by constraint adaptation and differential evolution. IEEE Trans Evol Comput 3:22-34.
143. Sutter JM, Jurs PC. 1996. Prediction of aqueous solubility for a diverse set of heteroatom-containing organic compounds using a quantitative structure-property relationship. J Chem Inf Comput Sci 36:100.
144. Szczepaniak PS, Lisoba PJG, Kacprzyk J. 2000. Fuzzy systems in medicine. Berlin: Physica-Verlag.
145. Tan P-N, Steinbach M, Kumar V. 2006. Introduction to data mining. Addison Wesley, Pearson International Edition.
146. 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 47:1688-1701. (Pubitemid 47210071)
147. Tetko IV, Tanchuk VY, Kasheva TN, Villa AEP. 2001. Estimation of aqueous solubility of chemical compounds using E-state indices. J Chem Inf Comput Sci 41:1488-1493.
148. Torres A, Nieto JJ. 2006. Fuzzy logic in medicine and bioinformatics. J Biomed Biotechnol 2:91908.
149. Tsaioun K, Jacewicz M. 2009. De-risking drug discovery with ADDME: avoiding drug development mistakes early. Altern Lab Anim 37(Suppl 1):47-55.
150. Tsaioun K, Bottlaender M, Mabondzo A. 2009. ADDME: avoiding drug development mistakes early. Central nervous system drug discovery perspective. BMC Neurol 9(Suppl 1):S1.
151. van de Waterbeemd H, Gifford E. 2003. Admet in silico modelling: towards prediction paradise? Nature Rev Drug Discov 2: 193-204.
152. Vapnik V. 1992. In: Moody JE, Hanson SJ, Lippmann RP, editors. Advances in neural information processing systems, vol. 4. San Francisco: Morgan Kaufmann Publishers, Inc.
153. Von Eschenbach AC. 2007. Statement Before the Senate Agriculture, Rural Development, Food and Drug Administration and Related Agencies Appropriations Subcommittee, U.S. Food and Drug Administration. www.fda.gov/ola/2007/ criticalpath060107.html (accessed 27 November 2007).
154. Votano JR, Parham M, Hall LH, Kier LB. 2004a. New predictors for several ADME/Tox properties: aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors. Mol Diversity 8:379-391. (Pubitemid 39491373)
155. Votano JR, Parham M, Hall LH, Kier LB, Hall LM. 2004b. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation. Chem Biodiversity 1:1829-1841.
156. Waller CL, Shah A, Nolte M. 2007. Strategies to support drug discovery through integration of systems and data. Drug Discov Today 12:634-639. (Pubitemid 47243705)
157. Wanchana S, Yamashita F, Hashida M. 2002. Quantitative structure/property relationship analysis on aqueous solubility using genetic algorithm-combined partial least squares method. Pharmazie 57: 127-129. (Pubitemid 34151530)
158. Wang J, Krudy G, Xie X-Q, Wu C, Holland G. 2006. Genetic algorithm-optimized QSPR models for bioavailability, protein binding, and urinary excretion. J Chem Inf Model 46: 2674-2683. (Pubitemid 46008136)
159. Webb A. 2002. Statistical pattern recognition. New York: John Wiley & Sons.
160. Weekes D, Fogel GB. 2003 Evolutionary optimization, back-propagation, and data preparation issues in QSAR modeling of HIV inhibition by HEPT derivatives. BioSystems 72:149-158. (Pubitemid 37464795)
161. Wegner JK, Zell A. 2003. Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J Chem Inf Comput Sci 43: 1077-1084.
162. Wegner JK, Frohlich H, Zell A. 2004. Feature selection for descriptor based classification methods 1: human intestinal absorption (HIA). J Chem Inf Comp Sci 44:931.
163. Wessel MD, Jurs PC, Tolan JW, Muskal SM. 1998. Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comp Sci 38:726-735. (Pubitemid 128571205)
164. White RE. 2000. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu Rev Pharmacol Toxicol 40:133-157. (Pubitemid 30340676)
165. Wishart DS. 2005. Bioinformatics in drug development and assessment. Drug Metab Rev 37:279-310. (Pubitemid 40677506)
166. Wishart DS. 2007. Improving early drug discovery through ADME modelling: an overview. Drugs RD 8:349-362. (Pubitemid 350014787)
167. Wolpert DH, Macready WG. 1997. No free lunch theorems for optimization. IEEE Trans Evol Comp 1:67-82. (Pubitemid 127771019)
168. Wunberg T, Hendrix M, Hillisch A, Lobell M, Meier H, Schmeck C, Wild H, Hinzen B. 2006. Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. Drug Discov Today 11:175-180. (Pubitemid 43375969)
169. Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, Chen YZ. 2004. Prediction of p-glycoprotein substrates by support vector machine approach. J Chem Inf Comp Sci 44:1630-1638.
170. Yaffe D, Cohen Y, Espinosa G, Arenas A, Giralt F. 2001. A fuzzy ARTMAP based on quantitative structure-property relationships (QSPRs) for predicting aqueous solubility of organic compounds. J Chem Inf Comput Sci 41:1177.
171. Yamashita F, Hashida M. 2004. Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery. Drug Metab Pharmacokin 19:327-338.
172. Yamashita F, Wanchana S, Hashida M. 2002. Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method. J Pharm Sci 91:2230-2239.
173. Yamashita F, Fujiwara S-I, Wanchana S, Hashida M. 2006. Quantitative structure/activity relationship modelling of pharmacokinetic properties using genetic algorithm-combined partial least squares method. J Drug Target 14:496-504. (Pubitemid 44592901)
174. Yan A, Gasteiger J. 2003. Prediction of aqueous solubility of organic compounds based on a 3D structure representation. J Chem Inf Comput Sci 43:429-434.
175. Yao X. 1999. Evolving artificial neural networks. Proc IEEE 87: 1423-1447. (Pubitemid 30503424)
176. Yao X, Liu H, Zhang R, Liu M, Hu Z, Panaye A, Doucet JP, Fan B. 2005. Qsar and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol Pharm 2:348-356. (Pubitemid 41503675)
177. Yap CW, Chen YZ. 2005. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J Pharm Sci 94:153-168. (Pubitemid 40116379)
178. Yap CW, Xue Y, Li H, Li ZR, Ung CY, Han LY, Zheng CJ, Cao ZW, Chen YZ. 2006. Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini Rev Med Chem 6:449-459.
179. Yap CW, Li H, Ji ZL, Chen YZ. 2007. Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties. Mini Rev Med Chem 7:1097-1107. (Pubitemid 350200216)
180. Yoshida K, Niwa T. 2006. Quantitative structure-activity relationship studies on inhibition of HERG potassium channels. J Chem Inf Model 46:1371-1378. (Pubitemid 43999180)
181. Young MB, DiSilvestro MR, Sendera TJ, Freund J, Kriete A, Magnuson SR. 2003. Analysis of gene expression in carbon tetrachloride-treated rat livers using a novel bioarray technology. Pharmacogenomics J 3:41-52. (Pubitemid 36432465)
182. Yuan H, Wang Y, Cheng Y. 2007. Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity. J Chem Inf Model 47:159-169.
183. Zadeh LA. 1965. Fuzzy sets. Inform Control 8:338-353.
184. Zadeh LA. 1968. Fuzzy algorithms. Inform Control 12:94-102.
185. Zernov VV, Balakin KV, Ivaschenko AA, Nikolay P, Savchuk NP, Pletnev IV. 2003. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 43: 2048-2056.
186. Zhang H, Singer B. 2005. Recursive partitioning in the health sciences. Berlin: Springer.
187. Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT. 2006. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 217:105-119. (Pubitemid 41773826)
188. Zhao YH, Abraham MH, Ibrahim A, Fish PV, Cole S, Lewis ML, de Groot MJ, Reynolds DP. 2007. Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J Chem Inf Model 47:170-175. (Pubitemid 46225573)
189. Zheng CJ, Han LY, Yap CW, Ji ZL, Cao ZW, Chen YZ. 2006. Therapeutic targets: progress of their exploration and investigation of their characteristics. Pharmacol Rev 58:259-279.
190. Zmuidinavicius D, Didziapetris R, Japertas P, Avdeef A, Petrauskas A. 2003. Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption. J Pharm Sci 92:621.