Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

Molecular Diversity

Volumn 15, Issue 1, 2011, Pages 269-289

  Fernandez, Michael ^a   Caballero, Julio ^b   Fernandez, Leyden ^c   Sarai, Akinori ^a  

^a KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^b UNIVERSIDAD DE TALCA   (Chile)

^c BARCELONA SUPERCOMPUTING CENTER   (Spain)

Author keywords

Drug design; Enzyme inhibition; Feature selection; In silico modeling; QSAR; Review; SAR; Structure activity relationships

Indexed keywords

ACETAMIDE DERIVATIVE; BENZIMIDAZOLE DERIVATIVE; CALCIUM CHANNEL; CHYMOTRYPSIN; CRUZIPAIN; CYCLIN DEPENDENT KINASE; GLYCOPROTEIN P; GONADORELIN AGONIST; GONADORELIN ANTAGONIST; HUMAN IMMUNODEFICIENCY VIRUS PROTEINASE; LYSOZYME; MATRIX METALLOPROTEINASE; POTASSIUM CHANNEL; PROTEIN FARNESYLTRANSFERASE; PYRIMIDINE DERIVATIVE; TACRINE DERIVATIVE;

ALZHEIMER DISEASE; APOPTOSIS; ARTIFICIAL NEURAL NETWORK; BAYESIAN REGULARIZED GENETIC NEURAL NETWORK; BINDING AFFINITY; BLOOD BRAIN BARRIER; CHEMICAL MODEL; CHROMOSOME IDENTIFICATION; COMPUTER PROGRAM; DRUG ABSORPTION; DRUG BIOAVAILABILITY; DRUG DESIGN; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG PENETRATION; DRUG SOLUBILITY; ENZYME ACTIVITY; GENE POOL; GENETIC ALGORITHM; GENETIC ALGORITHM OPTIMIZED SUPPORT VECTOR MACHINE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS; INTESTINE ABSORPTION; MATHEMATICAL MODEL; MITOCHONDRIAL TOXICITY; NEOPLASM; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN LOCALIZATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; REVIEW; SUPPORT VECTOR MACHINE; TORSADE DES POINTES; TRYPANOSOMA CRUZI;

ALGORITHMS; BAYES THEOREM; DRUG DESIGN; GENETICS; HUMANS; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 79953327578     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-010-9234-9     Document Type: Review

References (105)

1. Gasteiger J (2006) Chemoinformatics: a new field with a long tradition. Anal Bioanal Chem 384:57-64. doi:10.1007/s00216-005-0065-y
2. Cramer RD, Patterson DE, Bunce JD (1988) Comparativemolecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959-5967. doi:10.1021/ja00226a005
3. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37:4130-4146. doi:10.1021/jm00050a010 (Pubitemid 24379702)
4. Folkers G, Merz A, Rognan D (1993) CoMFA: scope and limitations. In: Kubinyi H (ed) 3D-QSAR in drug design. Theory, methods and applications. ESCOM Science Publishers BV, Leiden pp 583-618
5. Hansch C, Kurup A, Garg R, Gao H (2001) Chem-bioinformatics and QSAR: a review of QSAR lacking positive hydrophobic terms. Chem Rev 101:619-672. doi:10.1021/cr0000067 (Pubitemid 35377680)
6. Sabljic A (1990) Topological indices and environmental chemistry. In: Karcher W, Devillers J (eds) Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology. Kluwer, Dordrecht pp 61-82
7. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027-1043. doi:10.1021/cr950202r (Pubitemid 126641476)
8. Livingstone DJ, Manallack DT, Tetko IV (1997) Data modelling with neural networks: advantages and limitations. J Comput Aid Mol Des 11:135-142. doi:10.1023/A:1008074223811 (Pubitemid 127508824)
9. Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 26:5-14. doi:10.1016/S0097-8485(01)00094-8 (Pubitemid 33034554)
10. Caballero J, Fernández M (2006) Linear and non-linear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. J Mol Model 12:168-181. doi:10.1007/s00894-005-0014-x (Pubitemid 43090583)
11. Holland H (1975) Adaption in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI
12. Cartwright HM (1993) Applications of artificial intelligence in chemistry. Oxford University Press, Oxford
13. Cho SJ, Hermsmeier MA (2002) Genetic algorithm guided selection: variable selection and subset selection. J Chem Inf Comput Sci 42:927-936. doi:10.1021/ci010247v (Pubitemid 35355242)
14. Duchowicz PR, Vitale MG, Castro EA, Fernandez M, Caballero J (2007) QSAR analysis for heterocyclic antifungals. BioorgMed Chem 15:2680-2689. doi:10.1016/j.bmc.2007.01.039 (Pubitemid 46389586)
15. Fernández M, Caballero J, Fernández L, Abreu JI, Garriga M (2007) Protein radial distribution function (P-RDF) and Bayesian- regularized genetic neural networks formodeling protein conformational stability: chymotrypsin inhibitor 2 mutants. J Mol Graph Model 26:748-759. doi:10.1016/j.jmgm.2007.04.011 (Pubitemid 47628843)
16. + channel by some triarylmethanes using topological charge indexes descriptors. J Comput Aid Mol Des 19:771-789. doi:10.1007/s10822-005-9025-z
17. Caballero J, Garriga M, Fernández M (2006) 2D Autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks. Bioorg Med Chem 14:3330-3340. doi:10.1016/j.bmc.2005.12.048
18. Caballero J, Tundidor-Camba A, Fernández M (2007) Modeling of the inhibition constant (Ki ) of some Cruzain ketonebased inhibitors using 2D spatial autocorrelation vectors and data-diverse ensembles of Bayesian-regularized genetic neural networks. QSAR Comb Sci 26:27-40. doi:10.1002/qsar.200610001 (Pubitemid 46202936)
19. Fernández M, Tundidor-Camba A, Caballero J (2005) Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo [3,4-d] pyrimidine derivatives using artificial neural networks ensembles. J Chem Inf Model 45:1884-1895. doi:10.1021/ci050263i (Pubitemid 41784762)
20. Fernández M, Caballero J (2006) Bayesian-regularized genetic neural networks applied to the modeling of non-peptide antagonists for the human luteinizing hormone-releasing hormone receptor. J Mol Graph Model 25:410-422. doi:10.1016/j.jmgm.2006.02.005 (Pubitemid 44667581)
21. Fernandez M, Carreiras MC, Marco JL, Caballero J (2006) Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized genetic neural networks and ensemble averaging. J Enzym Inhib Med Chem 21:647-661. doi:10.1080/14756360600862366 (Pubitemid 44970953)
22. Fernández M, Caballero J (2006) Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. BioorgMed Chem 14:280-294. doi:10.1016/j.bmc.2005.08.022 (Pubitemid 41635490)
23. Fernández M, Caballero J, Tundidor-Camba A (2006) Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl) amino]acetamide derivatives as matrix metalloproteinase inhibitors. Bioorg Med Chem 14:4137-4150. doi:10.1016/j.bmc.2006.01.072
24. Fernández M, Caballero J (2006) Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines. Chem Biol Drug Des 68:201-212. doi:10.1111/j.1747-0285.2006.00435.x (Pubitemid 44760245)
25. González MP, Caballero J, Tundidor-Camba A, Helguera AM, Fernández M (2006) Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches. Bioorg Med Chem 14:200-213. doi:10.1016/j.bmc.2005.08.009
26. 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 Aid Mol Des 21:207-221. doi:10.1007/s10822-006-9098-3 (Pubitemid 46510844)
27. So S, Karplus M (1996) Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. J Med Chem 39:1521-1530. doi:10.1021/jm9507035 (Pubitemid 26108787)
28. Gao H (2001) Application of BCUT metrics and genetic algorithm in binary QSAR analysis. J Chem Inf Comput Sci 41:402-407. doi:10.1021/ci000306p
29. (+) ion channels from 3Dpseudo-folding graph representation of protein sequences using genetic algorithm-optimized support vector machines. J Mol Graph Model 26:1306-1314. doi:10.1016/j.jmgm.2008.01.001
30. Caballero J, Fernández L, Garriga M, Abreu JI, Collina S, Fernández M (2007) Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines. J Mol Graph Model 26:166-178. doi:10.1016/j.jmgm.2006.11.002 (Pubitemid 46977255)
31. Hemmateenejad B, Miri R, Akhond M, Shamsipur M (2002) QSAR study of the calcium channel antagonist activity of some recently synthesized dihydropyridine derivatives. An application of genetic algorithm for variable selection in MLR and PLS methods. Chemom Intell Lab 64:91-99. doi:10.1016/S0169-7439(02)00068-0
32. Hemmateenejad B, Akhond M, Miri R, Shamsipur M (2003) Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous). J Chem Inf Comput Sci 43:1328-1334. doi:10.1021/ci025661p
33. Hemmateenejad B (2004) Optimal QSAR analysis of the carcinogenic activity of drugs by correlation ranking and genetic algorithm-based PCR. J Chemom 18:475-485. doi:10.1002/cem.891
34. 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 91:2230-2238. doi:10.1002/jps.10214
35. Selwood DL, Livingstone DJ, Comley JCW, O'Dowd AB, Hudson AT, Jackson P, Jandu KS, Rose VS, Stables JN (1990) Structure- activity relationships of antifilarial antimycin analogues: a multivariate pattern recognition study. J Med Chem 33:136-142. doi:10.1021/jm00163a023 (Pubitemid 20038183)
36. Ren Y, Liu H, Li S, Yao X, Liu M (2007) Prediction of binding affinities to b1 isoform of human thyroid hormone receptor by genetic algorithm and projection pursuit regression. Bioorg Med Chem Lett 17:2474-2482. doi:10.1016/j.bmcl.2007.02.025 (Pubitemid 46507788)
37. Turner DB, Willett P (2000) Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA-GA). J Comput Aid Mol Des 14:1-21. doi:10.1023/A:1008180020974 (Pubitemid 30103819)
38. Xue L, Bajorath J (2000) Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J Chem Inf Comput Sci 40:801-809. doi:10.1021/ci000322m
39. Kamphausen S, Höltge N, Wirsching F, Morys-Wortmann C, Riester D, Goetz R, Thürk M, Schwienhorst A (2002) Genetic algorithm for the design of molecules with desired properties. J Comput Aid Mol Des 16:551-567. doi:10.1023/A:1021928016359 (Pubitemid 36204611)
40. Guo W, Cai W, Shao X, Pan Z (2005) Application of genetic stochastic resonance algorithm to quantitative structure-activity relationship study. Chemom Intell Lab 75:181-188. doi:10.1016/j.chemolab.2004.07.004 (Pubitemid 40164341)
41. Teixido M, Belda I, Rosello X, Gonzalez S, Fabrec M, Llora X, Bacardite J, Garrelle JM, Vilaro S, Albericio F, Giralta E (2003) Development of a genetic algorithm to design and identify peptides that can cross the blood-brain barrier 1. Design and validation in silico. QSAR Comb Sci 22:745-753. doi:10.1002/qsar.200320004 (Pubitemid 37391072)
42. So SS, Karplus M (1997) Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks: 1. Method and validations. J Med Chem 40:4347-4359. doi:10.1021/jm970487v
43. So SS, Karplus M (1997) Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks: 2. Applications. J Med Chem 40:4360-4371. doi:10.1021/jm970488n
44. Chiu TL, So SS (2003) Genetic neural networks for functional approximation. QSAR Comb Sci 22:519-526. doi:10.1002/qsar.200310004
45. Patankar SJ, Jurs PC (2000) Prediction of IC50 values for ACAT inhibitors from molecular structure. J Chem Inf Comput Sci 40:706-723. doi:10.1021/ci990125r
46. Kauffman GW, Jurs PC (2000) Prediction of inhibition of the sodium ion-proton antiporter by benzoylguanidine derivatives from molecular structure. J Chem Inf Comput Sci 40:753-761. doi:10.1021/ci9901237
47. Kauffman GW, Jurs PC (2001) QSAR and k-nearest neighbor classification analysis of selective cyclooxygenase-2 inhibitors using topologically-based numerical descriptors. J Chem Inf Comput Sci 41:1553-1560. doi:10.1021/ci010073h
48. Mattioni BE, Jurs PC (2002) Development of quantitative structure- activity relationship and classification models for a set of carbonic anhydrase inhibitors. J Chem Inf Comput Sci 42:94-102. doi:10.1021/ci0100696 (Pubitemid 35355329)
49. Bakken GA, Jurs PC (2001) QSARs for 6-azasteroids as inhibitors of human type 1 5alpha-reductase: prediction of binding affinity and selectivity relative to 3-BHSD. J Chem Inf Comput Sci 41:1255-1265. doi:10.1021/ci010036q
50. Patankar SJ, Jurs PC (2002) Prediction of glycine/NMDA receptor antagonist inhibition from molecular structure. J Chem Inf Comput Sci 42:1053-1068. doi:10.1021/ci010114+ (Pubitemid 35360288)
51. Burden FR, Winkler DA (1999) Robust QSAR models using Bayesian regularized neural networks. J Med Chem 42:3183-3187. doi:10.1021/jm980697n (Pubitemid 29384055)
52. Winkler DA, Burden R (2004) Bayesian neural nets for modeling in drug discovery. Biosilico 2:104-111. doi:10.1016/S1741-8364(04)02393-5
53. MATLAB 7.0. Program (2004) MathWorks Inc., Natick. http:// www.mathworks.com
54. The MathWorks Inc (2004) Genetic algorithm and direct search toolbox user's guide for use with MATLAB. TheMathworks Inc., Natick
55. The MathWorks Inc. (2004) Neural network toolbox user's guide for use with MATLAB. The Mathworks Inc., Natick
56. Mackay DJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4:448-472. doi:10.1162/neco.1992.4.3.448
57. Mackay DJC (1992) Bayesian interpolation. Neural Comput 4:415-447
58. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273-297
59. Burges CJC (1998) Atutorial on support vector machines for pattern recognition. Data Min Knowl Discipl 2:1-47. doi:10.1023/A:1009715923555
60. Fröhlich H, Chapelle O, Schölkopf B (2003) Feature selection for support vector machines by means of genetic algorithms. In: Proceedings of 15th IEEE international conference on tools with AI, Sacramento, CA, USA, pp 142-148. doi:10.1109/TAI.2003.1250182
61. Yang SY, Huang Q, Li LL, Ma CY, Zhang H, Bai R, Teng QZ, Xiang ML, Wei YQ (2009) An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs. Artif Intell Med 46:155-163. doi:10.1016/j.artmed.2008.07.001
62. Ma CY, Yang SY, Zhang H, Xiang ML, Huang Q, Wei YQ (2008) Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA-CG-SVM method. J Pharmaceut Biomed 47:677-682. doi:10.1016/j.jpba.2008. 03.023
63. Zhang H, Chen QY, Xiang ML, Ma CY, Huang Q, Yang SY (2009) in silico prediction of mitochondrial toxicity by using GA-CGSVM approach. Toxicol in Vitro 23:134-140. doi:10.1016/j.tiv.2008.09.017
64. Chih-Chung C, Chih-Jen L (2001) LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm
65. Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20:269-276. doi:10.1016/S1093-3263(01)00123-1
66. Afantitis A, Melagraki G, Sarimveis H, Igglessi-Markopoulou O, Kollias G (2009) A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas. Eur J Med Chem 44:877-884. doi:10.1016/j.ejmech. 2008.05.028
67. Agrafiotis DK, Cedeño W, Lobanov VS (2002) On the use of neural network ensembles in QSAR and QSPR. J Chem Inf Comput Sci 42:903-911. doi:10.1021/ci0203702 (Pubitemid 35355240)
68. Caballero J, Fernández L, Abreu JI, Fernández M (2006) Amino acid sequence autocorrelation vectors and ensembles of Bayesian- regularized genetic neural networks for prediction of conformational stability of human lysozyme mutants. J Chem Inf Model 46:1255-1268. doi:10.1021/ci050507z (Pubitemid 43999169)
69. Fernández L, Caballero J, Abreu JI, Fernández M (2007) Amino acid sequence autocorrelation vectors and bayesian-regularized genetic neural networks for modeling protein conformational stability: gene V protein mutants. Proteins 67:834-852. doi:10.1002/prot.21349
70. MOPAC 6.0. (1993) Frank J. Seiler Research Laboratory. US Air Force Academy, Springs, CO
71. Fernández M, Caballero J (2007) QSAR models for predicting the activity of non-peptide luteinizing hormone-releasing hormone (LHRH) antagonists derived from erythromycin A using quantum chemical properties. J Mol Model 13:465-476. doi:10.1007/s00894-006-0163-6 (Pubitemid 46404030)
72. Fernández M, Caballero J (2007) QSAR modeling of matrix metalloproteinase inhibition by N-hydroxy-α-phenylsulfonylacetamide derivatives. Bioorg Med Chem 15:6298-6310. doi:10.1016/j.bmc.2007.06.014 (Pubitemid 47096793)
73. 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 15:7746-7754. doi:10.1016/j.bmc.2007.08.057 (Pubitemid 47575917)
74. Todeschini R, Consonni V, Pavan M (2002) DRAGON, version 2.1. Talete SRL, Milan, Italy
75. Cerius2, Version 4.11, http://www.accelrys.com
76. VCCLAB, Virtual Computational Chemistry Laboratory (2005) http://www.vcclab.org
77. Fernandez M, Abreu JI (2006) PROTMETRICS; version 1.0. Molecular Modeling Group University of Matanzas, Matanzas, Cuba
78. Kawashima S, Kanehisa M (2000) AAindex: amino acid index database. Nucleic Acids Res 28:374-374. doi:10.1093/nar/28.1.374
79. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59-69. doi:10.1007/BF00337288
80. Dykens JA, Will Y (2007) The significance of mitochondrial toxicity testing in drug development. Drug Discov Today 12:777-785. doi:10.1016/j.drudis. 2007.07.013 (Pubitemid 47362789)
81. Foye WO (1995) Cancer chemotherapeutic agents. American Chemical Society, Washington, DC
82. Ashkenazi A, Dixit VM (1998) Death receptors: signaling and modulation. Science 281:1305-1308. doi:10.1126/science.281.5381.1305 (Pubitemid 28406815)
83. Nagata S (1997) Apoptosis by death factor. Cell 88:355-365. doi:10.1016/S0092-8674(00)81874-7
84. Bartus RT, Dean RL, Beer B, Lippa AS (1982) The cholinergic hypothesis of geriatric memory dysfunction. Science 217:408-417. doi:10.1126/science.7046051 (Pubitemid 12048290)
85. Radic Z, Reiner E, Taylor P (1991) Role of the peripheral anionic site on acetylcholinesterase: inhibition by substrates and coumarin derivatives. Mol Pharmacol 39:98-104
86. Pang YP, Quiram P, Jelacic T, Hong F, Brimijoin S (1996) Highly potent, selective, and lowcost bis-tetrahydroaminacrine inhibitors of acetylcholinesterase: steps towar novel drugs for treating Alzheimer's disease. J Biol Chem 271:23646-23649. doi:10.1074/jbc.271.39.23646
87. Katz RA, Skalka AM (1994) The retroviral enzymes. Annu Rev Biochem 63:133-173. doi:10.1146/annurev.bi.63.070194.001025 (Pubitemid 24218631)
88. Kempf DJ, Marsh KC, Denissen JF, McDonald E, Vasavanonda S, Flentge CA, Green BE, Fino L, Park CH, Kong XP, Wideburg NE, Saldivar A, Ruiz L, Kati WM, Sham HL, Robins T, Stewart KD, Hsu A, Plattner JJ, Leonard JM, Norbeck DW (1995) ABT-538 is a potent inhibitor of human immunodeficiency virus protease and has high oral bioavailability in humans. Proc Natl Acad Sci USA 92:2484-2488. doi:10.1073/pnas.92.7.2484
89. Reddy P, Ross J (1999) Amprenavir: a protease inhibitor for the treatment of patients with HIV-1 infection. Formulary 34:567-577 (Pubitemid 29334848)
90. Vacca JP, Dorsey BD, Schleif WA, Levin RB, McDaniel SL, Darke PL, Zugay J, Quintero JC, Blahy OM, Roth E, Sardana VV, Schlabach AJ, Graham PI, Condra JH, Gotlib L, Holloway MK, Lin J, Chen IW, Vastag K, Ostovic D, Anderson PS, Emini EA, Huff JR (1994) L-735,524: an orally bioavailable human immunodeficiency virus type 1 protease inhibitor. Proc Natl Acad Sci USA 91:4096-4100. doi:10.1073/pnas.91.9.4096 (Pubitemid 24139580)
91. Castle NA (1999) Recent advances in the biology of small conductance calcium-activated potassium channels. Perspect Drug Discov Des 15:131-154. doi:10.1023/A:1017095519863
92. Vergara C, LaTorre R, Marrion NV, Adelman JP (1998) Calcium-activated potassium channels. Curr Opin Neurobiol 8:321-329. doi:10.1016/S0959-4388(98) 80056-1 (Pubitemid 28288566)
93. + channel, IKCa1: a potential immunosuppressant. Proc Natl Acad Sci USA 97:8151-8156. doi:10.1073/pnas.97.14. 8151
94. Engel JC, Doyle PS, Palmer J, Hsieh I, Bainton DF, McKerrow JH (1998) Growth arrest of T. cruzi by cysteine protease inhibitors is accompanied by alterations in Golgi complex and ER ultrastructure. J Cell Sci 111:597-606 (Pubitemid 28142364)
95. Zhang H, Xiang ML, Ma CY, Huang Q, Li W, Xie Y, Wei YQ, Yang SY (2009) Three-class classification models of logS and logP derived by using GA-CG-SVM approach. Mol Divers 13:261-268. doi:10.1007/s11030-009-9108-1
96. Ramosde Armas R, Gonzalez-Díaz H, Molina R, Uriarte E (2004) Markovian backbone negentropies: molecular descriptors for protein research. I. predicting protein stability in Arc repressor mutants. Proteins 56:715-723. doi:10.1002/prot.20159
97. Gonzalez-Diaz H, Molina R, Uriarte E (2005) Recognition of stable protein mutantswith 3Dstochastic average electrostatic potentials. FEBS Lett 579:4297-4301. doi:10.1016/j.febslet.2005.06.065 (Pubitemid 41116355)
98. González-Díaz H, Vilar S, Santana L, Uriarte E (2007) Medicinal chemistry and bioinformatics-current trends in drugs discovery with networks topological indices. Curr Top Med Chem 7:1015-1029. doi:10.2174/156802607780906771 (Pubitemid 47040529)
99. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E (2008) QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 29:2613-2622. doi:10.1002/jcc.21016
100. Munteanua CR, González-Díaz H, Magalhãesa AL (2008) Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J Theor Biol 254:476-482. doi:10.1016/j.jtbi.2008.06.003
101. Fernández M, Caballero J, Fernández L, Abreu JI, Acosta G (2008) Classification of conformational stability of protein mutants from 3D pseudo folding graph representation of protein sequences using support vector machines. Proteins 70:167-175. doi:10.1002/prot.21524 (Pubitemid 350293458)
102. Li ZC, Zhou XB, Lin YR, Zou XY (2008) Prediction of protein structure class by coupling improved genetic algorithm and support vector machine. Amino Acids 35:581-590. doi:10.1007/s00726-008-0084-z
103. Huang WL, Tung CW, Huang HL, Hwang SF, Ho SY (2007) Pro-Loc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. BioSystems 90:573-581. doi:10.1016/j.biosystems.2007.01.001
104. Block P, Paern J, Huallermeier E, Sanschagrin P, Sotriffer CA, Klebe G (2006) Physicochemical descriptors to discriminate protein- protein interactions in permanent and transient complexes selected by means of machine learning algorithms. Proteins 65:607-622. doi:10.1002/prot.21104 (Pubitemid 44583211)
105. Kernytsky A, Rost B (2009) Using genetic algorithms to select most predictive protein features. Proteins 75:75-88. doi:10.1002/prot.22211