Machine learning techniques and drug design

Current Medicinal Chemistry

Volumn 19, Issue 25, 2012, Pages 4289-4297

  Gertrudes, J C ^a   Maltarollo, V G ^b   Silva, R A ^a   Oliveira, P R ^a   Honorio K M ^a,b   Da Silva, A B F ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSIDADE FEDERAL DO ABC   (Brazil)

Author keywords

Drug design; Machine learning; Medicinal chemistry; QSAR

Indexed keywords

3 HYDROXYPYRIDINE 4 ONE; ANTIINFECTIVE AGENT; MELANOCORTIN 4 RECEPTOR; UNCLASSIFIED DRUG; VESICULAR MONOAMINE TRANSPORTER 2;

ANTIBACTERIAL ACTIVITY; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; COUNTER PROPAGATION NEURAL NETWORK; DEALKYLATION; DOSE RESPONSE; DRUG ABSORPTION; DRUG CONJUGATION; DRUG DESIGN; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG HYDROLYSIS; DRUG METABOLISM; DRUG OXIDATION; DRUG TARGETING; HUMAN; HYDROGEN BOND; INTERMETHOD COMPARISON; MACHINE LEARNING; MULTIPLE LINEAR REGRESSION ANALYSIS; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; PERCEPTRON; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; REVIEW; SELF ORGANIZING MAP; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; DRUG DESIGN; HUMANS; MODELS, MOLECULAR; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84866686849     PISSN: 09298673     EISSN: 1875533X     Source Type: Journal    
DOI: 10.2174/092986712802884259     Document Type: Review

References (148)

1. De Benedetti, P.G.; Fanelli, F. Computational quantum chemistry and adaptive ligand modeling in mechanistic QSAR. DDT, 2010, 15(19-20), 859-866.
2. Castillo-Garit, J.A.; Abad, C.; Rodríguez-Borges, J.E.; Marrero-Ponce, Y.; Torrens, F. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis. Curr. Top. Med. Chem., 2012, 12(8), 852-65.
3. Sharma, O.P.; Saini, N.K.; Gupta, V.; Sachdeva, K.; Arya, H. Evolutionary History of QSAR: A Review. J. Natur. Cons., 2011, 1(4), 266-272.
4. Oprea, T.I. On the information content of 2D and 3D descriptors for QSAR. J. Braz. Chem. Soc., 2002, 13(6), 811-815.
5. Tavares, L.C. QSAR: a abordagem de Hansch. Quim. Nova, 2004, 27(4), 631-639.
6. Montanari, M.L.C.; Montanari, C.A.; Gaudio, A.C. Validação lateral em relações quantitativas entre estrutura e atividade farmacológica. Quim. Nova, 2002, 25(2), 231-240.
7. Gaudio, A.C.; Zandonade, E. Proposição, validaç ão e análise dos modelos que correlacionam estrutura química e atividade biológica. Quim. Nova, 2001, 24(5), 658-671.
8. Arroio, A.; Honório, K.M.; da Silva, A.B.F. da. Propriedades químicoquânticas empregadas em estudos das relações estrutura-atividade. Quim. Nova, 2010, 33(3), 694-699.
9. Kiralj, R.; Ferreira, M.M.C. Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J. Braz. Chem. Soc., 2009, 20(4), 770-787.
10. Ferreira, M.M.C. Multivariate QSAR. J. Braz. Chem. Soc., 2002, 13(6), 742-753.
11. Goudarzi, N.; Goodarzi, M.; Chen, T. QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions. Med. Chem. Res., 2012, 21(4), 437-443.
12. Xu, J.; Wang, L.; Wang, L.; Shen, X.; Xu, W. QSPR Study of Setschenow Constants of Organic Compounds Using MLR, ANN, and SVM Analyses. J. Comp. Chem., 2011, 32(15), 3241-3252.
13. Alves, C.N.; Pinheiro, J.C.; Camargo, A.J.; Ferreira, M.M.C.; Ramero, R.A.; da Silva, A.B.F. A multiple linear regression and partial least squares study of flavonoid compounds with anti-HIV activity. J. Mol. Struct. THEOCHEM, 2001, 541(1-3), 81-88.
14. Yu, Y.; Su, R.; Wang, L.; Qi, W.; He, Z. Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques. Med. Chem. Res., 2010, 19(9), 1233-1244.
15. Nandi, S.; Bagchi, M.C. Activity Prediction of Some Nontested Anticancer Compounds Using GA-Based PLS Regression Models. Chem. Biol. Drug Des., 2011, 78(4), 587-595.
16. Noolvi, M.N.; Patel, H.M.; Bhardwaj V.A. Comparative QSAR Analysis of Quinazoline Analogues as Tyrosine Kinase (erbB-2) Inhibitors. Med. Chem., 2011, 7(3), 200-212.
17. Weber, K.C.; Honório, K.M.; Bruni, A.T.; Andricopulo, A.D.; da Silva, A.B.F. A partial least squares regression study with antioxidant flavonoid compounds. Struct. Chem., 2006, 17(3), 307-313.
18. Garcia, T.S.; Honorio, K.M. Two-Dimensional Quantitative Structure-Activity Relationship Studies on Bioactive Ligands of Peroxisome Proliferator-Activated Receptor delta. J. Braz. Chem. Soc., 2011, 22(1), 65-72.
19. Weber, K.C.; Honório, K.M.; Andricopulo, A.D.; da Silva, A.B.F. Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT1A receptor ligands. Eur. J. Med. Chem., 2010, 45(4), 1508-1514.
20. Maltarollo, V.G.; Homem-de-Mello P.; Honório K.M. Role of physicochemical properties in the activation of peroxisome proliferator- activated receptor delta. J. Mol. Model., 2011, 17(10), 2549-2558.
21. Witten, I.H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 2005.
22. Moss, G.P.; Shah, A.J.; Adams, R.G.; Davey, N.; Wilkinson, S.C.; Pugh, W.J.; Sun, Y. The application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancers. Eur. J. Pharm. Sci., 2012, 45(1-2), 116-127.
23. Yang, X.G.; Lv, W.; Chen, Y.Z.; Xue, Y. In Silico Prediction and Screening of gamma-Secretase Inhibitors by Molecular Descriptors and Machine Learning Methods. J. Comp. Chem., 2010, 31(6), 1249-1258.
24. Honório, K.M.; de Lima, E.F.; Quiles, M.G.; Romero, R.A.F.; Molfetta, F.A.; da Silva, A.B.F. Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds. Chem. Biol. Drug Des., 2010, 75(6), 632-640.
25. Molfetta, F.A.; Angelotti, W.F.D.; Romero, R.A.F.; Montanari, C.A.; da Silva, A.B.F. A neural networks study of quinone compounds with trypanocidal activity. J. Mol. Model., 2008, 14(10), 975-985.
26. Hameed, A.J.; Ibrahim, M.; ElHaes, H. Computational notes on structural, electronic and QSAR properties of Fulleropyrrolidine-1-carbodithioic acid 2; 3 and 4-substituted-benzyl esters. J. Mol. Struct. THEOCHEM, 2007, 809(1-3), 131-136.
27. Essa, A.H.; Ibrahem, M.; Hameed, A.J.; Al-Masoudi, N.A. Theoretical investigation of 3'-subtituted-2'-3'-dideoxythymidines related to AZT. QSAR infrared and substituent electronic effect studies. ARKIVOC, 2008, xiii, 255-265.
28. Ibrahim, M.; Saleh, N.A.; Hameed, A.J.; Elshemey, W.M.; Elsayed, A.A. Structural and Electronic Properties of new Fullerene Derivatives and their Possible Application as HIV-1 Protease Inhibitors. Spectrochim. Acta A, 2010, 75(2), 702-709.
29. Ibrahim, M.; Saleh, N.A.; Elshemey, W.M.; Elsayed, A.A. Fullerene Derivative as anti-HIV Protease Inhibitor: Molecular Modeling and QSAR Approaches. Mini Rev. Med. Chem., 2012, 12(6), 447-451.
30. Garcia, I.; Fall, Y.; Gomez, G.; Gonzalez-Diaz, H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol. Divers., 2011, 15(2), 561-567.
31. Casanola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T.H.; Khan, S.B.; Torrens, F.; Perez-Jimenez, F.; Rescigno, A.; Abad, C. Bond-Based 2D Quadratic Fingerprints in QSAR Studies: Virtual and In vitro Tyrosinase Inhibitory Activity Elucidation. Chem. Biol. Drug Des., 2010, 76(6), 538-545.
32. Goodarzi, M.; Freitas, M.P.; Vander Heyden, Y. Linear and nonlinear quantitative structure-activity relationship modeling of the HIV-1 reverse transcriptase inhibiting activities of thiocarbamates. Anal. Chim. Acta, 2011, 705(1-2), 166-173.
33. Qin, Y.; Deng, H.; Yan, H.; Zhong, R. An accurate nonlinear QSAR model for the antitumor activities of chloroethylnitrosoureas using neural networks. J. Mol. Graph. Model., 2011, 29(6), 826-833.
34. Fu, G.H.; Cao, D.S.; Xu, Q.S.; Li, H.D.; Liang, Y.Z. Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors. J. Chemometr., 2011, 25(2), 92-99.
35. Hoskins, J.C.; Himmelbau, D.M. Artificial Neural Network Models of Knowledge Representation in Chemical Engineering. Comput. Chem. Eng., 1988, 12(9-10), 881-890.
36. Zhou, P.; Tian, F.; Chen, X.; Shang, Z. Modeling and Prediction of Binding Affinities Between the Human Amphiphysin SH3 Domain and Its Peptide Ligands Using Genetic Algorithm-Gaussian Processes. Biopolymers, 2008, 90(6), 792-802.
37. Soyguder, S. Intelligent control based on wavelet decomposition and neural network for predicting of human trajectories with a novel vision-based robotic. Expert Syst. Appl., 2011, 38(11), 13994-14000.
38. Aitkenhead, M.J.; McDonald, A.J.S. The state of play in machine/ environment interactions. Artif. Intell. Rev., 2006, 25(3), 247-276.
39. Fogel G.B. Computational intelligence approaches for pattern discovery in biological systems. Brief Bioinform., 2008, 9(4), 307-316.
40. Perlovsky, L.I. Toward physics of the mind: Concepts, emotions, consciousness, and symbols. Phys. Life. Rev., 2006, 3(1), 23-55.
41. Fernandez, C.; Soria, E.; Martin, J.D.; Serrano, A.J. Neural networks for animal science applications: Two case studies. Expert Syst. Appl., 2006, 31(2), 444-450.
42. Cammann, H.; Jung, K.; Meyer, H.A.; Stephan, C. Avoiding Pitfalls in Applying Prediction Models, As Illustrated by the Example of Prostate Cancer Diagnosis. Clin. Chem., 2011, 57(11), 1490-1498.
43. Seker, H.; Odetayo, M.O.; Petrovic, D.; Naguib, R.N.G.; Bartoli, C.; Alasio, L.; Lakshmi, M S.; Sherbet, G.V. Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: Statistical, neural network and fuzzy approaches. Anticancer Res., 2002, 22(1A), 433-438.
44. Li H.; Yap, C. W.; Xue, Y.; Li, Z.R.; Ung, C.Y.; Han, L.Y.; Chen Y.Z. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents. Drug Develop. Res., 2005, 66(4), 245-259.
45. Mager, D.E.; Shirey, J.D.; Cox, D.; Fitzgerald, D.J.; Abernethy, D.R. Mapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network. J. Pharm. Sci., 2005, 94(11), 2475-2486.
46. Schneider, G.; Coassolo, P.; Lavé, T. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J. Med. Chem., 1999, 42(25), 5072-5076.
47. Guo, J.; Rao, N. Predicting Protein Folding Rate From Amino Acid Sequence. J. Bioinform. Comp. Biol., 2011, 9(1), 1-13.
48. He, J.Y.; Hu, H.J.; Harrison, R.; Tai, P.C.; Pan, Y. Transmembrane segments prediction and understanding using support vector machine and decision tree. Expert Syst. Appl., 2006, 30(1), 64-72.
49. Jalali-Heravi, M.; Shahbazikhah, P.; Zekavat, B.; Ardejani, M.S. Principal component analysis-ranking as method for the simulation of 13 C nuclear a variable selection ma netic resonance spectra of xanthones using artificial neural networks. QSAR Comb. Sci., 2007, 26(6), 764-772.
50. Reddy, A.S.; Kumar, S.; Garg, R. Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition. J. Mol. Graph. Model., 2010, 28(8), 852-862.
51. Carafa, R.; Faggiano, L.; Real, M.; Munne, A.; Ginebreda, A.; Guasch, H.; Flo, M.; Tirapu, L.; von der Ohe, P.C. Water toxicity assessment and spatial pollution patterns identification in a Mediterranean River Basin District. Tools for water management and risk analysis. Sci. Tot. Environ., 2011, 409(20), 4269-4279.
52. Sprous, D.G.; Palmer, R.K.; Swanson, J.T.; Lawless, M. QSAR in the Pharmaceutical Research Setting: QSAR Models for Broad, Large Problems. Curr. Top. Med. Chem., 2010, 10(6), 619-637.
53. Milac, A.L.; Avram, S.; Petrescu, A.J. Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors Application to HIV-1 protease inhibitors. J. Graph. Mol. Model., 2006, 25(1), 37-45.
54. Wu, C.H. ; McLarty, J.W. Neural Networks and Genome Informatics, Elsevier, 2000.
55. Ghosh, P.; Bagchi, M.C. QSAR Modeling for Quinoxaline Derivatives using Genetic Algorithm and Simulated Annealing Based Feature Selection. Curr. Med. Chem., 2009, 16(30), 4032-4048.
56. Caballero, J.; Fernandez, M. Artificial Neural Networks from MATLAB (R) in Medicinal Chemistry. Bayesian-Regularized Genetic Neural Networks (BRGNN): Application to the Prediction of the Antagonistic Activity Against Human Platelet Thrombin Receptor (PAR-1). Curr. Top. Med. Chem., 2008, 8(18), 1580-1605.
57. Douali, L.; Villemin, D.; Cherqaoui, D. Neural networks: Accurate nonlinear QSAR model for HEPT derivatives. J. Chem. Inf. Comp. Sci., 2003, 43(4), 1200-1207.
58. Schneider, P.; Tanrikulu, Y.; Schneider, G. Self-Organizing Maps in Drug Discovery: Compound Library Design, Scaffold-Hopping, Repurposing. Curr. Med. Chem., 2009, 16(3), 258-266.
59. Kohonen, T. Self-Organizing Maps. 3rd Ed. Springer. 2000.
60. Balbinot, L.; Smichowski, P.; Farias, S.; Arruda, M.A.Z.; Vodopivez, C.; Poppi, R.J. Classification of Antarctic algae by applying Kohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry. Spectrochim. Acta B, 2005, 60(5), 725-730.
61. Tan, C.; Qin, X.; Li, M. An ensemble method based on a self-organizing map for near-infrared spectral calibration of complex beverage samples. Anal. Bioanal. Chem., 2008, 392(3), 515-521.
62. Vracko, M. Kohonen Artificial Neural Network and Counter Propagation Neural Network in Molecular Structure-Toxicity Studies. Curr. Comput-Aid. Drug., 2005, 1(1), 73-78.
63. Guha, R.; Serra, J.R.; Jurs, P.C. Generation of QSAR sets with a selforganizing map. J. Mol. Graph. Model., 2004, 23(1), 1-14.
64. Hoshi, K.; Kawakami, J.; Kumagai, M.; Kasahara, S.; Nishimura, N.; Nakamura, H.; Sato, K. An analysis of thyroid function diagnosis using Bayesiantype and SOM-type neural networks. Chem. Pharm. Bull., 2005, 53(12), 1570-1574.
65. Nandi, S.; Vracko, M.; Bagchi, M.C. Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks. Chem. Biol. Drug Des., 2007, 70(5), 424-436.
66. Xiao, Y.D.; Clauset, A.; Harris, R.; Bayram, E.; Santago, P.; Schmitt, J.D. Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets. J. Chem. Inf. Model., 2005, 45(6), 1749-1758.
67. Yan, A.; Wang, Z.; Cai, Z. Prediction of human intestinal absorption by GA feature selection and support vector machine regression. Int. J. Mol. Sci., 2008, 9(10), 1961-1976.
68. Hornik, K.; Stinchcombe M.; White H. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2(5), 359-366.
69. Kovacs, Z.L. Redes Neurais Artificiais. 3rd Ed. Livraria da Física. 2002.
70. Haykin, S.S. Neural networks: a comprehensive foundation, 2nd Ed. Prentice Hall, 1999.
71. Zheng, F.; Zheng, G.; Deaciuc, A.G.; Zhan, C.G.; Dwoskin, L.P.; Crooks, P.A. Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2. Bioorg. Med. Chem., 2007, 15(8), 2975-2992.
72. Kuzmanovski, I.; Novi, M. Counter-propagation neural networks in Matlab. Chemometr. Intell. Lab., 2008, 90(1), 84-91.
73. Tso, B.; Mather, P.M. Classification Methods for Remotely Sensed Data. CRC Press. 2009.
74. Mlinsek, G.; Novi, M.; Hodoscek, M.; Solmajer, T. Prediction of Enzyme Binding: Human Thrombin Inhibition Study by Quantum Chemical and Artificial Intelligence Methods Based on X-ray Structures. J. Chem. Inf. Comp. Sci., 2001, 41(3-6), 1286-1294.
75. Sabet, R.; Fassihi, A.; Hemmateenejad, B.; Saghaei, L.; Miri, R.; Gholami, M. Computer-aided design of novel antibacterial 3-hydroxypyridine-4- ones: application of QSAR methods based on the MOLMAP approach. J. Comput-Aid. Mol. Des., 2012, 26(3), 349-361.
76. Minovski, N.; Vrako, M.;Šolmajer, T. Quantitative structure-activity relationship study of antitubercular fluoroquinolones. Mol. Divers., 2011, 15(2), 417-426.
77. Bishop, C.M. Neural Networks for Pattern Recognition. Oxford University Press, 2005.
78. Winkler, D.A.; Burden, F.R. Application of Neural Networks to Large Dataset QSAR, Virtual Screening, and Library Design. In:Combinatorial Library, Methods in Molecular Biology . English, L. B. (Eds.), Springer Protocols, 2002, 325-367.
79. de Freitas, J.F.G. Bayesian Methods for Neural Networks. PhD thesis, Cambridge University Engineering Department, 2003.
80. Neal, R.M. Bayesian Learning for Neural Networks. PhD thesis, University of Toronto, 1995.
81. Fernandez, M.; Caballero, J. Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. Bioorg. Med. Chem., 2006, 14(1), 280-294.
82. Neal, R.M. Bayesian Learning for Neural Networks, Lecture Notes in Statistics, Springer: New York, 1996.
83. Caballero, J.; Fernandez, M.; Gonzalez-Nilo, F.D. Structural requirements of pyrido[2, 3-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation, CoMFA and CoMSIA analyses. Bioorg. Med. Chem., 2008, 16(11), 6103-6115.
84. Ajay; Walters, W.P.; Murcko, M.A. Can We Learn To Distinguish between "Drug-like" and "Nondrug-like" Molecules? J. Med. Chem., 1998, 41(18), 3314-3324.
85. Wang, Y.; Li, Y.; Li, Y.; Yang, S.; Yang, L. Modeling Km values using electrotopological state: Substrates for cytochrome P450 3A4-mediated metabolism. Bioorg. Med. Chem. Letters., 2005, 15(18), 4076-4084.
86. Burden, F.R.; Ford, M.G.; Whitley, D.C.; Winkler, D.A. Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks. J. Chem. Inf. Comp. Sci., 2000, 40(6), 1423-1430.
87. Schölkopf, B.; Burges, C.J.C.; Smola, A.J. Advances in kernel methods: support vector learning. MIT Press: Cambridge, 1999.
88. Taylor, J.S.; Cristianini, N. An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2005.
89. Boser, E.; Vapnik, N.; Guyon, I. M. Training Algorithm Margin for Optimal Classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory. 1992, 6, 144-152.
90. Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc., 1998, 2(2), 121-167.
91. Louis, B.; Agrawal, V.K.; Khadikar, P.V. Prediction of intrinsic solubility of generic drugs using mlr, ann and svm analyses. Eur. J. Med. Chem., 2010, 45(9), 4018-4025.
92. Palmer, A.M. New horizons in drug metabolism, pharmacokinetics and drug discovery. Drug News Perspect., 2003, 16(1), 57-62.
93. Ansel, H.C.; Popovich, N.G.; Loyd, V. Farmacotécnica: Formas Farmacêuticas e Sistemas de Liberação de Fármacos. 6th Ed. Premier, 2003.
94. Nigsch, F.; Klaffke, W.; Miret, S. In vitro models for processes involved in intestinal absorption. Expert. Opin. Drug Met., 2007, 3(4), 545-556.
95. Patrick, G.L. An introduction to Medicinal Chemistry. Oxford University Press, 2009.
96. Kennedy, T. Managing the drug discovery/development interface. DDT., 1997, 2(10), 436-444.
97. Qin, Y.; Deng, H.; Yan, H.; Zhong, R. An accurate nonlinear qsar model for the antitumor activities of chloroethylnitrosoureas using neural networks. J. Mol. Graph. Model., 2011, 29(6), 826-833.
98. Darnag, R.; Mostapha Mazouz, E.L.; Schmitzer, A.; Villemin, D.; Jarid, A.; Cherqaoui, D. Support vector machines: Development of qsar models for predicting anti-hiv-1 activity of tibo derivatives. Eur. J. Med. Chem., 2010, 45(4), 1590-1597.
99. Sorich, M.J.; Miners, J.O.; McKinnon, R.A.; Winkler, D.A.; Burden, F.R.; Smith, P.A. Comparison of linear and nonlinear classification algorithms for the prediction of drug and chemical metabolism by human udpglucuronosyltransferase isoforms. J. Chem. Inf. Comp. Sci., 2003, 43(6), 2019-2024.
100. Fatemi, M.H.; Heidari, A.; Ghorbanzade, M. Prediction of aqueous solubility of drug-like compounds by using an artificial neural network and leastsquares support vector machine. Bull. Chem. Soc. Jpn., 2010, 83(11), 1338-1345.
101. Pourbasheer, E.; Riahi, S.; Ganjali, M.R.; Norouzi, P. Qsar study on melanocortin-4 receptors by support vector machine. Eur. J. Med. Chem., 2010, 45(3), 1087-1093.
102. Kotsiantis, S.B. Supervised machine learning: A review of classification techniques. Informatica, 2007, 31(3), 249-268.
103. Thai, K.; Nguyen, T.; Ngo, T.; Tran, T.; Huynh, T. A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity. Molecules, 2012, 17(4), 4560-4582.
104. Doucet, J.P.; Barbault, F.; Xia, H.; Panaye, A.; Fan, B.T. Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design. Curr. Comput-Aid. Drug., 2007, 3(4), 263-289.
105. Yao, X.J.; Panaye, A.; Doucet, J.P.; Zhang, R.S.; Chen, H.F.; Liu, M.C.; Hu, Z.D.; Fan, B. T. Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression. J. Chem. Inf. Comput. Sci., 2004, 44(4), 1257-1266.
106. Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem., 2001, 26(1), 5-14.
107. Ivanciuc, O. Applications of Support Vector Machines in Chemistry. In: Lipkowitz, K. B.; Cundari, T. R. Reviews in Computational Chemistry. Wiley-VCH, John Wiley & Sons, Inc. 2007, 23, 291-400.
108. Yin, L.; Huang, C.H.; Ni, J. Clustering of gene expression data: Performance and similarity analysis. BMC Bioinformatics, 2006, 7(4), S19.
109. Schneider, G.; Wrede, P. Artificial neural networks for computer-based molecular design. Prog. Biophys. Mol. Biol., 1998, 70(3), 175-222.
110. Hussain, A.S. Artificial neural network based in vitro-in vivo correlations. Adv. Exp. Med. Biol., 1997, 423, 149-58.
111. Winkler, D.A. Neural networks in ADME and toxicity prediction. Drug Future, 2004, 29(10), 1043-1057.
112. Fogel, G.; Corne, D. Evolutionary computation in bioinformatics. Imprint: Morgan Kaufmann, 2002.
113. Jolliffe, I.T. Principal Component Analysis. 2nd Ed. Springer, 2002.
114. Cronin, M.T.D.; Schultz, T.W. Pitfalls in qsar. J. Mol. Struct. THEOCHEM, 2003, 622(1-2), 39-51.
115. Muegge, I.; Oloff, S. Advances in virtual screening. DDT., 2006, 3(4), 405-411.
116. Seifert, M.H.J. Targeted scoring functions for virtual screening. DDT., 2009, 14(11-12), 562-569.
117. Oloff, S.; Mailman, R.B.; Tropsha, A. Application of validated qsar models of d1 dopaminergic antagonists for database mining. J. Med. Chem., 2005, 48(23), 7322-7332.
118. Mishra, P.; Tripathi, V.; Yadav, B.S. Insilco qsar modeling and drug development process. GERF Bull. Biosci., 2010, 1(1), 37-40.
119. Shen, M.; Béguin, C.; Golbraikh, A.; Stables, J.P.; Kohn, H.; Tropsha, A. Application of predictive qsar models to database mining: identification and experimental validation of novel anticonvulsant compounds. J. Med. Chem., 2004, 47(9), 2356-2364.
120. Cherkasov, A.; Shi, Z.; Fallahi, M.; Hammond, G.L. Successful in silico discovery of novel nonsteroidal ligands for human sex hormone binding globulin. J. Med. Chem., 2005, 48(9), 3203-3213.
121. Tsai, K.C.; Chen, S.Y.; Liang, P.H.; Lu, I.L.; Mahindroo, N.; Hsieh, H.P.; Chao, Y.S.; Liu, L.; Liu, D.; Lien, W.; Lin, T.H.; Wu, S.Y. Discovery of a novel family of sars-cov protease inhibitors by virtual screening and 3d-qsar studies. J. Med. Chem., 2006, 49(12), 3485-3495.
122. Reddy, A.S.; Pati, S.P.; Kumar, P.P.; Pradeep, H.N.; Sastry, G.N. Virtual screening in drug discovery -a computational perspective. Curr. Prot. Pept. Sc., 2007, 8(4), 329-351.
123. Hu, L.; Chen, G.; Chau, R.M.W. A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors. J. Mol. Graph. Model., 2006, 24(4), 244-253.
124. Afantitis, A.; Melagraki, G.; Koutentis, P.A.; Sarimveis, H.; Kollias, G. Ligand -based virtual screening procedure for the prediction and the identification of novel -amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks. Eur. J. Med. Chem., 2011, 46(2), 497-508.
125. Noeske, T.; Trifanova, D.; Kauss, V.; Renner, S.; Parsons, C.G.; Schneider, G.; Weil, T. Synergism of virtual screening and medicinal chemistry: Identification and optimization of allosteric antagonists of metabotropic glutamate receptor 1. Bioorg. Med. Chem., 2009, 17(15), 5708-5715.
126. Schneider, G.; Nettekoven, M. Ligand-based combinatorial design of selective purinergic receptor (a2a) antagonists using self-organizing maps. J. Comb. Chem., 2003, 5(3), 233-237.
127. Karpov, P.V.; Osolodkin, D.I.; Baskin, I.I.; Palyulin, V.A.; Zefirov, N.S. One-class classification as a novel method of ligand-based virtual screening: The case of glycogen synthase kinase 3-inhibitors. Bioorg. Med. Chem. Lett., 2011, 21(22), 6728-6731.
128. Molnar, L.; Keseru, G.M. A neural network based virtual screening of cytochrome p450 3a4 inhibitors. Bioorg. Med. Chem. Lett., 2002, 12(3), 419-421.
129. Jorissen, R.N.; Gilson, M.K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model., 2005, 45(3), 549-561.
130. Plewczynski, D.; Spieser, S.A.; Koch, U. Assessing different classification methods for virtual screening. J. Chem. Inf. Model., 2006, 46(3), 1098-1106.
131. Lobanov, V. Using artificial neural networks to drive virtual screening of combinatorial libraries. DDT., 2004, 2(4), 149-156.
132. van de Waterbeemd, H.; Gifford, E. Admet in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2(3), 192-204.
133. Walker, D.K. The use of pharmacokinetic and pharmacodynamic data in the assessment of drug safety in early drug development. Brit. J. Clin. Pharmaco., 2004, 58(6), 601-608.
134. Xu, J.; Hagler, A. Chemoinformatics and drug discovery. Molecules, 2002, 7(8), 566-600.
135. Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification. J. Chem. Inf. Comput. Sci., 2003, 43(6), 1882-1889.
136. Ma, L.; Cheng, C.; Liu, X.; Zhao, Y.; Wang, A.; Herdewijn, P. A neural network for predicting the stability of RNA/DNA hybrid duplexes. Chem. Int. Lab. Sys., 2004, 70(2), 123-128.
137. Niwa, T. Prediction of Biological Targets Using Probabilistic Neural Networks and Atom-Type Descriptors. J. Med. Chem., 2004, 47(10), 2645-2650.
138. Taskinen, J.; Yliruusi, J. Prediction of physicochemical properties based on neural network modelling. Adv. Drug Deliv. Rev., 2003, 55(9), 1163-1183.
139. Niculescu, S.P. Artificial neural networks and genetic algorithms in QSAR. J. Mol. Struct. THEOCHEM, 2003, 622(1-2), 71-83.
140. Moda, T.L.; Honório, K.M.; Andricopulo, A.D. Propriedades farmacocinéticas no planejamento de fármacos. In: Química Medicinal: Métodos em Planejamento Racional de Fármacos. Montanari, C.A. (Org.). EDUSP: São Paulo, 2011, 515-556.
141. Belic, A.; Grabnar, I.; Belic, I.; Karba, R.; Mrhar, A. Predicting the antihypertensive effect of nitrendipine from plasma concentration profiles using artificial neural networks. Comput. Biol. Med., 2005, 35(10), 892-904.
142. Funar-Timofei, S.; Ionescu, D.; Suzuki, T. A tentative quantitative structuretoxicity relationship study of benzodiazepine drugs. Toxicol. In Vitro, 2010, 24(1), 184-200.
143. Jalali-Heravi, M.; Kyani, A. Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shufflingadaptive neuro fuzzy inference system and artificial neural networks. Chemosphere, 2008, 72(5), 733-740.
144. Huuskonen, J. Qsar modeling with the electrotopological state indices: predicting the toxicity of organic chemicals. Chemosphere, 2003, 50(7), 949-953.
145. Mazzatorta, P.; Vracko, M.; Jezierska, A.; Benfenati, E. Modeling toxicity by using supervised kohonen neural networks. J. Chem. Inf. Comp. Sci., 2003, 43(2), 485-492.
146. Singh, S.K.; Saini, S.; Verma B.; Mishra, D.N. Quantitative structure pharmacokinetic relationship using artificial neural network: A review. IJPSDR., 2009, 1(3), 144-153.
147. Stojic, N.; Eric, S.; Kuzmanovski, I. Prediction of toxicity and data exploratory analysis of estrogen-active endocrine disruptors using counterpropagation artificial neural networks. J. Mol. Graph. Model., 2010, 29(3), 450-460.
148. Winkler, D. Neural networks as robust tools in drug lead discovery and development. Mol. Biotechnol., 2004, 27(2), 139-167.