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Using artificial neural networks to predict cell-penetrating compounds

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Pages 783-796 | Published online: 24 May 2011

Bibliography

  • Arbib M. The hand book of brain theory and neural network. 2nd edition. MIT Press; 2002
  • Katritzky AR, Kuanar M, Slavov S, Quantitative correlation of physical and chemical properties with chemical structure: utility for prediction. Chem Rev 2010;110(10):5714-89
  • Manallack DT, Livingstone DJ. Neural networks in drug discovery: have they lived up to their promise? Eur J Med Chem 1999;34:195
  • Schneider G, Wrede P. Artificial neural networks for computer-based molecular design. Prog Biophys Mol Biol 1998;70:175
  • Turner JV, Maddalena DJ, Cutler DJ. Pharmacokinetic parameter prediction from drug structure using artificial neural networks. Int J Pharm 2004;270:209
  • MATLAB. Available from: http://www.mathworks.com/
  • Statistica. Available from: http://www.statsoft.com/#
  • Virtual Computational Laboratory. Available from: http://www.vcclab.org/lab/asnn/
  • Van de Waterbeemd H, Gifford E. ADMET in silico modeling: towards the prediction paradise? Nat Rev Drug Discov 2003;2:192
  • Saunders KC. Automatation and robotics in ADME screening. Drug Discov Today 2004;1:373
  • Lipinski CA. Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 2000;44:235
  • Stenberg P, Norinder U, Luthman K, Experimental and computational screening models for the prediction of intestinal drug absorption. J Med Chem 2001;44:1927
  • Zhao YH, Le J, Abraham MH, Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 2001;90:749
  • Zsila F, Iwao Y. The drug binding site of human alpha1-acid glycoprotein: insight from induced circular dichroism and electronic absorption spectra. Biochim Biophys Acta 2007;1770:797
  • Cucullo L, Hossain M, Rapp E, Development of a humanized in vitro blood-brain barrier model to screen for brain penetration of antiepileptic drugs. Epilepsia 2007;48:505
  • Garberg P, Ball M, Borg N, In vitro models for the blood-brain barrier. Toxicol In Vitro 2005;19:299
  • Yazdanian M, Glynn SL, Wright JL, Correlating partitioning and Caco-2 permeability of structurally diverse small molecular weight compounds. Pharm Res 1998;15:1490
  • Wichmann K, Diedenhofen M, Klamt A. Prediction of blood/brain partitioning and human serum albumin binding based on COSMO-RS s-moments. J Chem Inf Model 2007;47:228
  • Gunturi SB, Narayanan R. In silico ADME modeling 3: computational models to predict human intestinal absorption using sphere exclusion and kNN QSAR methods. QSAR Comb Sci 2007;26:653
  • Hall LM, Hall LH, Kier LB. Modeling of drug albumin binding affinity with E-State topological structure representation. J Chem Inf Comput Sci 2003;43:2120
  • Haykin S. Neural networks a comprehensive foundation. Edition. Pearson: 1999
  • Zupan J, Gasteiger J. Neural networks in chemistry and drug design. Wiley-VCH, Weinheim; 1999
  • Karelson M. Molecular descriptors in QSAR/QSPR. J Wiley & Sons; New York: 2000
  • Rumelhart DE, Hinton GE, Williams RJ. Learning representation by back-propagating errors. Nature 1986;323:533-6
  • Karelson M, Dobchev DA, Kulshyn OV, Neural networks convergence using physicochemical data. J Chem Inf Model 2006;46(8):1891-7
  • Sanchez VD. Special issue on RBF networks, Part I. Neurocomputing, 19; 1998
  • Sanchez VD. Special issue on RBF networks, Part II. Neurocomputing, 20; 1998
  • Mackay DJ. Probable networks and plausible predictions a review of practical bayesian methods for supervised neural networks. Comput Neural Syst 1995;6:469
  • Burden FR, Winkler DA. Robust QSAR models using bayesian regularized neural networks. J Med Chem 1999;42:3183-7
  • Holland J. Adaptation in natural and artificial systems. University of Michigan Press; 1975
  • Davis L. Handbook of Genetic Algorithms Van Nostrand-Reinhold; 1991
  • Judson R. Genetic algorithms and their use in chemistry. In: Boyd DB, Lipkowitz KB, editors, Reviews in computational chemistry. Wiley; 1997
  • Scholkopf B, Burges CJC, Smola AJ. Advances in kernel methods: support vector learning. MIT Press; Cambridge, Massachusetts: 1999
  • Breiman LE. Random forests. Mach Learn 2001;45:5-32
  • Breiman L, Friedman J, Stone C, Olshen R. Classification and regression trees. Chapman and Hall; New York: 1984
  • Halgren TA. MMFF VI. MMFF94s option for energy minimization studies. J Comput Chem 1999;20:720
  • Stewart JJ. P Mopac 60. Quant Chem Program Ex 1990;9:10
  • Dewar MJS, Zoebisch EG, Healy EF, Development and use of quantum mechanical molecular models. AM1: a new general purpose quantum mechanical molecular model. J Am Chem Soc 1985;107:3902
  • Brook RJ, Arnold GC. Applied regression analysis andexperimental design. Marcel Dekker, Statistics: textbook and monographs, Inc.; New York: 1985
  • Katritzky AR, Mu L, Lobanov VS, Correlation of boiling points with molecular structure. 1. A training set of 298 diverse organics and a test set of 9 simple inorganics. J Phys Chem 1996;100:10400
  • Rogers DR, Hopfinger AJ. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 1994;34:854
  • Luke BT. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 1994;34:1279
  • Sung-Sau S, Karplus M. Evolutionary optimization in quantitative structure activity relationship: an application of genetic neural networks. J Med Chem 1996;39:1521
  • Burden FR, Ford M, Whitley D, The use of automatic relevance determination in QSAR studies using Bayesian neural nets. J Chem Inf Comput Sci 2000;40:1423
  • Tetko IV, Livingstone DJ, Luik AI. Neural network studies: comparison of overfitting and overtraining. J Chem Inf Comput Sci 1995;35:826
  • Baskin I, Palyulin VA, Zefirov NS. Neural networks in building QSAR models. Methods Mol Biol 2009;458:133
  • Andrea TA, Kalayeh H. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 1991;34:2824
  • Yasri A, Hartsough D. Toward an optimal procedure for variable selection and QSAR model building. J Chem Inf Comput Sci 2001;41:1218
  • Smith DA, van de Waterbeemd H. Pharmacokinetics and metabolism in early drug discovery. Curr Opin Chem Biol 1999;3:373
  • Navia MA, Chaturvedi PR. Design principles for orally bioavailable drugs. Drug Dev Today 1996;1:179
  • Chan OH, Stewart BH. Physicochemical and drug-delivery considerations for oral drug bioavailability. Drug Dev Today 1996;1:461
  • Stewart BH, Chan OH, Jezyk N, Discrimination between drug candidates using models for evaluation of intestinal absorption. Adv Drug Deliv Rev 1997;23:27-45
  • Barthe L, Woodley J, Houin G. Gastrointestinal absorption of drugs: methods and studies. Fundam Clin Pharmacol 1999;13:154
  • Bohets H, Annaert P, Mannens G, Strategies for absorption screening in drug discovery and development. Curr Top Med Chem 2001;1:367
  • Bravo SA, Nielsen CU, Amstrup J, In depth evaluation of Gly-Sar transport parameters as a function of culture time in the Caco-2 cell model. Eur J Pharm Sci 2004;21:77
  • Kansy M, Senner F, Gubernator K. Physicochemical high throughput screening: parallel artificial membrane permeability assay in the description of passive absorption processes. J Med Chem 1998;41:1007
  • Thompson M, Krull UJ, Worsfold P. The structure and electrochemical properties of a polymer-supported lipid biosensor. J Anal Chim Acta 1980;117:133
  • Veber DF, Johnson SR, Cheng H-Y, Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002;45:2615
  • Kerns EH, Di L, Petuskey S, Combined application of parallel artificial membrane permeability assay and Caco-2 permeability assays in drug discovery. J Pharm Sci 2004;93:1440
  • Wessel MD, Mente S. ADME by computer. Ann Rep Med Chem 2001;36:257
  • Wessel MD, Jurs PC, Tolan JW, Prediction of human intestinal absorption of drug compounds from molecular structure. J Chem Inf Comput Sci 1998;38:726
  • Agatonovic-Kustrin S, Beresford R, Yusof APM. Theoretically-derived molecular descriptors important in human intestinal absorption. J Pharm Biomed Anal 2001;25:227
  • Wegner JK, Frohlich H, Zell A. Feature selection for descriptor based classification models 2 human intestinal absorption (HIA). J Chem Inf Comput Sci 2004;44:931
  • Niwa T. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures. J Chem Inf Comput Sci 2003;43:113
  • Wolohan PR, Clark RD. Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA. J Comput Aided Mol Des 2003;17:65
  • Polley MJ, Burden F, Winkler D. Predictive human intestinal absorption QSAR models using bayesian regularized neural networks. Aust J Chem 2005;58:859
  • Zhao YH, Le J, Abraham MH, Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 2001;90:749
  • Di Fenza A, Alagona G, Ghio C, Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach. J Comput Aided Mol Des 2007;21:207
  • Karelson M, Karelson G, Tamm T, QSAR study of pharamacological permeabilities. Arkivoc 2009;ii:218
  • Smith QR. A review of blood-brain barrier transport techniques. Methods Mol Med 2003;93:208
  • Jolliet-Riant P, Tillement JP. Drug transfer across the blood-brain barrier and improvement of brain delivery. Fundam Clin Pharmacol 1999;13:26
  • Vastag M, Keseru GM. Current in vitro and in silico models for blood-brain barrier penetration: a practical view. Curr Opin Drug Discov Devel 2009;12(1):115
  • Winkler DA, Burden FR. Modelling blood–brain barrier partitioning using Bayesian neural nets. J Mol Graph Model 2004;22:499
  • Garg P, Verma J. In silico prediction of blood brain barrier permeability: an artificial neural network model. J Chem Inf Model 2006;46:289
  • Wang Z, Yan A, Yuan Q. Classification of blood-brain barrier permeation by Kohonen's Self-Organizing Neural Network (KohNN) and Support Vector Machine (SVM). QSAR Combi Sci 2009;28:989
  • Zhao YH, Abraham MH, Ibrahim A, Predicting penetration across membrane from simple descriptors and fragmentation schemes. J Chem Inf Model 2007;47:170
  • Karelson M, Dobchev DA, Tamm T, Correlation of blood-brain penetration and human serum albumin binding with theoretical descriptors. Arkivoc 2008;xvi:38
  • Derossi D, Joliot AH, Chassaing G, The third helix of the Antennapedia homeodomain translocates through biological membranes. J Biol Chem 1994;8:10444
  • Vives E, Brodin P, Lebleu BA. Truncated HIV-1 Tat protein basic domain rapidly translocates through the plasma membrane and accumulates in the cell nucleus. J Biol Chem 1997;272:16010
  • Foged C, Nielsen HM. Cell-penetrating peptides for drug delivery across membrane barriers. Expert Opin Drug Deliv 2008;5:105
  • Meade BR, Dowdy SF. Enhancing the cellular uptake of siRNA duplexes following noncovalent packaging with protein transduction domain peptides. Adv Drug Deliv Rev 2008;60:530
  • Dobchev DA, Mager I, Tulp I, Prediction of cell-penetrating peptides using artificial neural networks. Curr Comput Aided Drug Des 2010;6:79
  • Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst 1987;2:37
  • Byvatov E, Fechner U, Sadovski J, Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 2003;43:1882

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