Application of neural networks in structure-activity relationships

Medicinal Research Reviews

Volumn 19, Issue 3, 1999, Pages 249-269

  Kövesdi, István ^a   Dominguez Rodriguez, Maria Felisa ^b   Ôrfi, László ^b   Náray Szabó, Gábor ^c   Varró, András ^d   Papp, Julius Gy ^d   Mátyus, Péter ^b  

^a EGIS PHARMACEUTICALS LTD   (Hungary)

^b SEMMELWEIS UNIVERSITY OF MEDICINE   (Hungary)

^c EÖTVÖS LORÁND UNIVERSITY   (Hungary)

^d UNIVERSITY OF SZEGED   (Hungary)

Author keywords

Benzodiazepine receptor; Class I antiarrhythmic agents; CoMFA; Neural networks; Structure activity relationships

Indexed keywords

AMIODARONE; ANTIARRHYTHMIC AGENT; BENZODIAZEPINE RECEPTOR; CIBENZOLINE; DETAJMIUM BITARTRATE; DISOPYRAMIDE; ENCAINIDE; FLECAINIDE; INDECAINIDE; LIDOCAINE; LORCAINIDE; MEXILETINE; MORACIZINE; PHENYTOIN; PRAJMALINE; PROCAINAMIDE; PROPAFENONE; QUINIDINE; TOCAINIDE;

ALGORITHM; ANTIARRHYTHMIC ACTIVITY; DRUG RECEPTOR BINDING; DRUG STRUCTURE; FEEDBACK SYSTEM; LIGAND BINDING; NERVE CELL NETWORK; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; STRUCTURE ACTIVITY RELATION; VALIDATION PROCESS;

DRUG DESIGN; NEURAL NETWORKS (COMPUTER); STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 0043146556     PISSN: 01986325     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1098-1128(199905)19:3<249::AID-MED4>3.0.CO;2-0     Document Type: Review

References (229)

1. Mátyus P, Varró A, Papp JGy, Wamhoff H, Varga I, Virág L. Antiarrhythmic agents - Current status and perspectives. Med Res Rev 1997;18:427-451.
2. Greco G, Novellino E, Silipo C, Vittoria A. Study of benzodiazepines receptor-sites using a combined QSAR-CoMFA approach. Quant Struct Act Rel 1992;11:461-477.
3. Famini GR, Wilson LY. Using theoretical descriptors in quantitative structure-property relationships -3-Carboxybenzisoxazole decarboxylation kinetics. J Chem Soc Perk T 2 1994;7:1641-1650.
4. Lowrey AH, Cramer CJ, Urban JJ, Famini GR. Quantum-chemical descriptors for linear solvation energy relationships. Comput Chem 1995;199:209-215.
5. Cronce TD, Famini GR, De Soto JA, Wilson LY. Using theoretical descriptors in quantitative structure-property relationships - Some distribution equilibria. J Chem Soc Perk T 1998;6:1293-1301.
6. Kier LB. Shape indices of orders one and three from molecular graphs. Quant Struct-Act Relat 1986;5: 1-7.
7. Basak SC, Grunwald GD. Molecular similarity and estimation of molecular properties. J Chem Inf Comput Sci 1995;35:366-372.
8. Kier LB. Molecular Orbital Theory in Drug Research. New York: Academic Press; 1971.
9. Crippen GM. A novel approach to calculation of conformation: distance geometry. J Comp Phys 1977;24:96-107.
10. Weinstein H. Chemical Applications of molecular electrostatic potentials. New York: Plenum Press; 1981.
11. Broto P, Moreau G, Vandycke C. Molecular-structures - Perception, auto-correlation descriptor and SAR studies - Auto-correlation descriptors. Eur J Med Chem 1984;19:66-70.
12. Mabilia M, Pearlstein RA, Hopfinger AJ. Molecular graphics and drug design. Amsterdam: Elsevier Science Publishers; 1981.
13. Gombar VK, Jain DVS. Quantification of molecular shape and its correlation with physico-chemical properties. Indian J Chem 1987;26A:554-555.
14. Famini GR, Wilson LY. Quantitative treatments of solute/solvent interactions. Amsterdam: Elsevier; 1994.
15. Lowrey AH, Famini GR. Using theoretical descriptors in quantitative structure-activity-relationships - HPLC capacity factors for energetic materials. Struct Chem 1995;6:357-365.
16. Rauhut G, Clark T. Electron-transfer reactions - AMI and Ab-initio studies on self-exchange in P-diaminobenzene systems. J Am Chem Soc 1993;115:9127-9135
17. Náray-Szabó G, Ferency Gy. Molecular electrostatics. Chem Rev 1995;4:829-847.
18. Wireko FC, Kellogg GE, Abraham DJ. Allosteric modifiers of hemoglobin. 2. Crystallographically determined binding-sites and hydrophobic binding interaction analysis of novel hemoglobin oxygen effectors. J Med Chem 1991;34:758-767.
19. Altomare C, Cellamare S, Carotti A, Casini G, Testa B. X-ray crystal-structure, partitioning behavior, and molecular modeling study of piracetam-type nootropics - Insights into the pharmacophore. J Med Chem 1995;38:170-179.
20. Fauchere JL, Quarendon P, Kaetterer L. Estimating and representing hydrophobicity potential. J Mol Graphics 1988;6:202-203.
21. Ghose AK, Crippen GM. Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J Chem Inf Comput Sci 1987;27:21-35.
22. Broto P, Moreau G, Vandycke C. Molecular-structures - Perception, auto-correlation descriptor and SAR studies - System of atomic contributions for the calculation of the normal-octanol water partition-coefficients. Eur J Med Chem 1984;19:71-78.
23. Ajay, Walters WP, Murcko MA. Can we learn to distinguish between drug-like and nondrug-like molecules. J Med Chem 1998;41:3314-3324.
24. Sadowski J, Kubinyi H. A scoring scheme for discriminating between drugs and nondrugs. J Med Chem 1998;41:3325-3329
25. Kubinyi H. QSAR, Hansch analysis and related approaches. In: Methods and principles in medicinal chemistry. Vol 1. Weinheim: VCH; 1993.
26. Topliss GJ, Edwards RP. Chance Factors in studies of quantitative structure-activity relationships. J Med Chem 1979;22:1238-1243.
27. Rawlings JO. Applied regression analysis: A research tool. Wadsworth & Brooks; 1988.
28. Mculloch WS, Pitts W. A logical calculus of ideas immanent in nervous activity. Bull Math Bioph 1943;5:115-133.
29. Rumelhart DE. Parallel distributed processing. London: MIT Press; 1982.
30. McClelland JL, Rumelhart DE. Explorations in parallel distributed processing: A handbook of models, programs and exercises. Cambridge: MIT Press; 1988.
31. Zupan J, Gasteiger J. Neural networks - A new method for solving chemical problems or just a passing phase. Anal Chim Acta 1991;248:1-30.
32. Manallack DT, Ellis DD, Livingstone DJ. Analysis of linear and nonlinear QSAR data using neural networks. J Med Chem 1994;37:3758-3767.
33. Hoskins JC, Himmelbau DM. Artificial neural network models of knowledge representation in chemical-engineering. Comput Chem Eng 1988;12:881-890.
34. Molnar B, Papik K, Schaefer R, Dombovari Z, Feher J, Tulassay Z. Application of neural networks in medical applications (in Hung.). Orvosi Hetilap 1998;139:3-9.
35. Bohm G. New approaches in molecular structure prediction. Biophys Chem 1996;59:1-32.
36. Guez A, Nevo I. Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring. Clin Chim Acta 1996;248:73-90.
37. Golden 3rd JB, Torgersen D, Tibbetts C. Pattern recognition for automated DNA sequencing: I. On-line signal conditioning and feature extraction for basecalling. ISMB 1993;1:136-144.
38. Astion ML, Wilding P. The application of backpropagation neural networks to problems in pathology and laboratory medicine. Arch Pathol Lab Med 1992;116:995-1001.
39. Jakus V. Utilization of artificial-intelligence in chemistry. Chem Listy 1993;87:262-279.
40. Friedel P. Neural networks in industry - A case-study. Ann Phys-Paris 1992;17:553-591.
41. Barnes FS. Some engineering models for interactions of electric and magnetic-fields with biological-systems. Bioelectromagnetics 1992;51:67-85.
42. Wythoff BJ. Backpropagation neural networks - A tutorial. Chemometr Intell Lab 1993;18:115-155.
43. Cambon B, Devillers J. New trends in structure-biodegradability relationships. Quant Struct-Act Rel 1993;12:49-56.
44. Bishop CM. Neural networks and their applications. Rev Sci Instrum 1994;65:1803-1832.
45. Becker S, Plumbley M. Unsupervised neural-network learning procedures for feature-extraction and classification. Appl Intell 1996;6:185-203.
46. Coetzee FM, Stonick VL. On a natural homotopy between linear and nonlinear single-layer networks. IEEE T Neural Networ 1996;7:307-317.
47. Qian N, Sejnowski J. Predicting the secondary structure of globular proteins using neural network models. Biophys J 1988;53:A98.
48. Bohr H, Bohr J, Brunak S, Cotterill R, Lautrup B, Norskov L, Olsen O, Petersen S. Protein secondary structure and homology by neural networks. The alpha helices in rhodopsin. FEBS Lett 1988:241:223-228.
49. Muggleton S, King RD, Sternberg MJ. Protein secondary structure prediction using logic-based machine learning. Protein Eng 1992;5:647-657.
50. Rabow AA, Scheraga HA. Lattice neural network minimization. Application of neural network optimization for locating the global-minimum conformations of proteins. J Mol Biol 1993;232:1157-1168.
51. Eisenhaber F, Imperiale F, Argos P, Frommel C. Prediction of secondary structural content of proteins from their amino-acid-composition alone. I. New analytic vector decomposition methods. Prot-Struct Funct Gen 1996;25:157-168.
52. Chen RS, Leung HW, Dong YC, Wong RN. Modeling of the three-dimensional structure of luffin-alpha and its simulated reaction with the substrate oligoribonucleotide GAGA. J Protein Chem 1996;15:649-657.
53. Fetrow JS, Palumbo MJ, Berg G. Patterns, structures, and amino acid frequencies in structural building blocks, a protein secondary structure classification scheme. Proteins 1997;27:249-271.
54. Riis, SK, Krogh A. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J Comput Biol 1996;3:163-183.
55. Qian H. Prediction of alpha-helices in proteins based on thermodynamic parameters from solution chemistry. J Mol Biol 1996;256:663-666.
56. Wythoff BJ, Levine SP, Tomellini SA. Spectral peak verification and recognition using a multilayered neural network. Anal Chem 1990;62:2702-2709.
57. Meyer B, Hansen T, Nute D, Albersheim P, Darvill A, York W, Sellers J. Identification of the 1H-NMR spectra of complex oligosaccharides with artificial neural networks. Science 1991;251:542-544.
58. Tanabe K, Tamura T, Uesaka H. Neural network system for the identification of infrared spectra. Appl Spectrosc 1992;46:807-810.
59. Robb EW, Munk ME. A neural network approach to infrared spectrum interpretation. Microchim Acta 1990;1:131-155.
60. Munk ME, Madison MS, Robb EW. Neural network models for infrared spectrum interpretation. Microchim Acta 1991;2:505-514.
61. Kvasnicka V, Sklenak S, Pospichal J. Application of recurrent neural networks in chemistry prediction and classification of C 13 NMR chemical shifts in a series of monosubstituted benzenes. J Chem Inf Comp Sci 1992;32:742-747.
62. Anker LS, Jurs PC. Prediction of C 13 nuclear magnetic resonance chemical shifts by artificial neural networks. Anal Chem 1992;64:1157-1164.
63. Panaye A, Doucet JP, Fan BT, Feuilleabois E, Elazzouzzi SR. Artificial neural network simulation of C 13 NMR shifts for methyl substituted cyclohexanes. Chemometr Intell Lab 1994;24:129-135.
64. Miyashita Y, Yoshida H, Yaegashi O, Kimura T, Nishiyama H, Sasaki S. Nonlinear modeling of C 13 NMR chemical shift data using artificial neural networks and partial least squares method. J Mol Struct (Theochem) 1994;311:241-245.
65. Cherniak R, Valafar H, Moms LC, Valafar F. Cryptococcus neoformans chemotyping by quantitative analysis of 1H nuclear magnetic resonance spectra of glucuronoxylomannans with a computer-simulated artificial neural network. Clin Diagn Lab Immun 1998;5:146-159.
66. Ala-Korpela M, Hiltunen Y, Bell JD. Quantification of biomedical NMR data using artificial neural network analysis: Lipoprotein lipid profiles from 1H NMR data of human plasma. NMR Biomed 1995;8:235-244.
67. Dalmas B, Bannister WH. Prediction of protein secondary structure from circular dichroism spectra: an attempt to solve the problem of the best-fitting reference protein subsets. Anal Biochem 1995:225:39-48.
68. Powell DA, Turula V, de Haseth JA, van Halbeek H, Meyer B. Sulfate detection in glycoprotein-derived oligosaccharides by artificial neural network analysis of Fourier-transform infrared spectra. Anal Biochem 1994;220:20-27.
69. Dwarakanath S, Ferris CD, Pierre JW, Asplund RO, Curtis DL. A neural network approach to the early detection of cancer. Biomed Sci Instrum 1994;30:239-243.
70. Freeman R, Goodacre R, Sisson PR, Magee JG, Ward AC, Lightfoot NF rapid identification of species within the mycobacteriurn tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra. J Med Microbiol 1994;40:170-173.
71. Veselis RA, Reinsel R, Wronski M. Analytical methods to differentiate similar electroencephalographic spectra: Neural network and discriminant analysis. J Clin Monitor 1993;9:257-267.
72. Lin CW, LaManna JC, Takefuji Y. Quantitative measurement of two-component pH-sensitive colorimetric spectra using multilayer neural networks. Biol Cybern 1992;67:303-308.
73. Kvasnicka V, Sklenak S, Pospichal J. Neural networks in chemistry. 2. Applications. Chem Listy 1993;87:157-163.
74. Meyer M, Meyer K, Robert H. Neural networks for interpretation of infrared-spectra using extremely reduced spectral data. Anal Chim Acta 1993;282:407-415.
75. Corne SA, Fisher J, Johnson AP, Newell WR. Cross-peak classification in 2-dimensional nuclear-magnetic-resonance spectra using a 2-layer neural-network. Anal Chim Acta 1993;278:149-158.
76. Smits JRM, Schoenmakers P, Stehmann A, Sijstermans F, Kateman G. Interpretation of infrared-spectra with modular neural-network systems. Chemometr Intell Lab 1993;18:27-39.
77. West GMJ. Predicting phosphorus NMR shifts using neural networks. J Chem Inf Comp Sci 1993;33:577-589.
78. Souza MN, Figueira MA, Dacosta MS. An artificial neural network applied to the identification of Mossbauer-Spectra of corrosion products. Nucl Instrum Meth B 1993;73:95-100.
79. Gasteiger J, Zupan J. Neural networks in chemistry. Angew Chem-Int Edit 1993;32:503-527.
80. Villemin D, Cherqaoui D, Mesbah A. Predicting carcinogenicity of polycyclic aAromatic-hydrocarbons from backpropagation neural-network. J Chem Inf Comp Sci 1994;34:1288-1293.
81. Cherqaoui D, Villemin A, Mesbah A, Cense JM, Kvasnicka V. Use of a neural-network to determine the normal boiling points of acyclic ethers, peroxides, acetals and their sulfur analogs. J Chem Soc-Faraday T 1994;90:2015-2019.
82. Schierle C, Otto M. Qualitative and semiquantitative analysis in ICP-AES using multivariate calibration. Microchim Acta 1994;113:357-372.
83. Souza MN, Gatts C, Figueira MA. Application of the artificial neural-network approach to the recognition of specific patterns in Auger electron-spectroscopy. Surf Interface Anal 1993;20:1047-1050.
84. Jasper WJ, Kovacs ET. Using neural networks and NIR spectrophotometry to identify fibers. Text Res J 1994;64:444-448.
85. Wessling M, Mulder MHV, Vanderlinden M, Bos M, Vanderlinden WE, Bos A. Modeling the permeability of polymers - A neural-network approach. J Membrane Sci 1994;86:193-198.
86. Glorieux C, Thoen J. Thermal depth profile reconstruction by neural-network recognition of the photothermal frequency-spectrum. J Appl Phys 1996;80:6510-6515.
87. Pancoska P, Janota V, Keiderling TA. Interconvertibility of electronic and vibrational circular-dichroism spectra of proteins - A test of principle using neural-network mapping. Appl Spectrosc 1996;50:658-668.
88. Ruisanchez I, Potokar P, Zupan J. Classification of energy dispersion X-ray-spectra of mineralogical samples by artificial neural networks. J Chem Inf Comp Sci 1996;36:214-220.
89. Long JR, Mayfield HT, Henley MV, Kromann PR. Pattern recognition of jet fuel chromatographic data by artificial neural networks with back propagation of error. Anal Chem 1991;63:1256-1261.
90. Peterson KL. Counter propagation neural networks in the modeling and prediction of Kovats indexes for substituted phenols. Anal Chem 1992;64:379-386.
91. Grunenberg J, Herges R. Prediction of chromatographic retention values (R(M)) and partition coefficients (Log P Oct) using a combination of semiempirical self consistent reaction field calculations and neural networks. J Chem Inf Comp Sci 1995;35:905-911.
92. Dettmar HP, Barbour GS, Blackwell KT, Vogl TP, Alkon DL, Fry Jr FS, Totah JE, Chambers TL. Orange juice classification with a biologically based neural network. Comput Chem 1996;20:261-266.
93. Goodacre R, Kell DB. Correction of mass spectral drift using artificial neural networks. Anal Chem 1996;68:271-280.
94. Miao H, Yu M, Hu S. Artificial neural networks aided deconvolving overlapped peaks in chromatograms. J Chromatogr A 1996;749:5-11.
95. Cupid BC, Nicholson JK, Davis P, Ruane RJ, Wilson ID, Glen RC, Rose VS, Beddell CR, Lindon JC. Quantitative structure chromatography relationships in reversed-phase high-performance liquid-chromatography - Prediction of retention behavior using theoretically derived molecular-properties. Chromatographia 1993;37:241-249.
96. Bell SE, Mead WC, Jones RD, Eiceman GA, Ewing RG. Connectionist hyperprism neural-network for the analysis of ion mobility spectra - An empirical-evaluation. J Chem Inf Comp Sci 1993;33:609-615.
97. Casale JF, Watterson JW. A computerized neural network method for pattern-recognition of cocaine signatures. J Forensic Sci 1993;38:292-301.
98. Wessel MD, Jurs PC. Prediction of reduced ion mobility constants from structural information using multiple linear-regression analysis and computational neural networks. Anal Chem 1994;66:2480-2487.
99. Mittermayr CR, Drouen ACJH, Otto M, Grasserbauer M. Neural networks for library search of ultraviolet-spectra. Anal Chim Acta 1994;294:227-242.
100. Smits JRM, Melssen WJ, Daalmans GJ, Kateman G. Using molecular representations in combination with neural networks - A case-study - Prediction of the HPLC retention index. Comput Chem 1994;18:157-172.
101. Wessel MD, Sutter M, Jurs PC. Prediction of reduced ion mobility constants of organic-compounds from molecular-structure. Anal Chem 1996;68:4237-4243.
102. Mulholland M, Hibbert DB, Haddad PR, Parslov P. A comparison of classification in artificial-intelligence, induction versus a self-organizing neural networks. Chemometr Intell Lab 1995;30:117-128.
103. Galvan IM, Zaldivar JM, Hernandez H, Molga E. The use of neural networks for fitting complex kinetic data. Comput Chem Eng 1996;20:1451-1465.
104. Goodacre R, Timmins EM, Rooney PJ, Rowland JJ, Kell DB. Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbancy Fourier-Transform infrared-spectroscopy and artificial neural networks. FEMS Microbiol Lett 1996;140:233-239.
105. Mulholland M, Preston P, Hibbert DB, Haddad PR, Compton P. Teaching a computer ion chromatography from a database of published methods. J Chromatogr A 1996;739:15-24.
106. Jimenez O, Garcia MA, Marina ML. Neural-network capability for retention modeling in micellar liquid-chromatography with hybrid eluents. J Liq Chromatogr RT 1997;20:731-742.
107. Sacchero G, Bruzzoniti MC, Sarzanini C, Mentasti E, Melting HJ, Coenegracht PMJ. Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography. J Chromatogr A 1998;799:35-45.
108. Marengo E, Gennaro MC, Angelino S. Neural-network and experimental-design to investigate the effect of 5 factors in ion-interaction high-performance liquid-chromatography. J Chromatogr A 1998;799:47-55.
109. Bodor N, Harget A, Huang M. Neural network studies. I. Estimation of the aqueous solubility of organic compounds. J Am Chem Soc 1991;113:9480-9483.
110. Egolf LM, Jurs PC. Prediction of boiling points of organic heterocyclic-compounds using regression and neural-network techniques. J Chem Inf Comp Sci 1993;33:616-625.
111. Sigman ME, Rives SS. Prediction of atomic ionization potentials I-III Using an artificial neural network. J Chem Inf Comp Sci 1994;34:617-620.
112. Gakh AA, Gakh ER, Sumpter BG, Nord DW. Neural-network graph-theory approach to the prediction of the physical-properties of organic-compounds. J Chem Inf Comp Sci 1994;34:832-839.
113. Chastrette M, Cretin D, Aidi C. Structure odor relationships Using neural networks in the estimation of Camphoraceous or fruity odors and olfactory thresholds of aliphatic alcohols. J Chem Inf Comp Sci 1996;36:108-113.
114. Breindl A, Beck B, Clark T, Glen RC. Prediction of the n-octanol/water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network. J Mol Model 1997;3:142-155.
115. El-Deredy W, Ashmore SM, Branston NM, Darling JL, Williams SR, Thomas DG. Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks. Cancer Res 1997;57:4196-4197.
116. Arle JE, Morriss C, Wang ZJ, Zimmerman RA, Phillips PG, Sutton LN. Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks. J Neurosurg 1997;86:755-761.
117. Lohmann R, Schneider G, Wrede E. Structure optimization of an artificial neural filter detecting membrane-spanning amino acid sequences. Biopolymers 1996;38:13-29.
118. Ritschel WA, Akileswaran R, Hussain AS. Application of neural networks for the prediction of human pharmacokinetic parameters. Method Find Exp Clin Pharm 1995;17:629-643.
119. Gobburu JV, Shelver WH. Quantitative structure-pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks. J Pharm Sci 1995;84:862-865.
120. Xu Y, Mural R, Shah M, Uberbacher E. Recognizing exons in genomic sequence using GRAIL II. Genet Eng 1994;16:241-253.
121. Lohmann R, Schneider G, Behrens D, Wrede P. A neural network model for the prediction of membrane-spanning amino acid sequences. Protein Sci 1994;3:1597-1601.
122. Schneider G, Wrede P. Development of artificial neural filters for pattern recognition in protein sequences. J Mol Evol 1993;36:586-595.
123. Adler B, Ammon K, Dobers S, Winterstein M, Ziesmer H. Prediction of carcinogen properties of amino-biphenyls by computer-applications. Chem Tech-Leipzig 1992;44:363-367.
124. Schneider G, Wrede P. The rational design of amino-acid-sequences by artificial neural networks and simulated molecular evolution - De-novo design of an idealized leader peptidase cleavage site. Biophys J 1994;66:335-344.
125. Cherqaoui D, Villemin D, Kvasnicka V. Application of neural-network approach for prediction of some thermochemical properties of alkanes. Chemometr Intell Lab 1994;24:117-128.
126. Chan WM, Nascimento CAO. Use of neural networks for modeling of olefin polymerization in high-pressure tubular reactors. J Appl Polym Sci 1994;53:1277-1289.
127. Bodor N, Huang MJ, Harget A. Neural-network studies. 3. Prediction of partition-coefficients. J Mol Struc-(Theochem) 1994;115:259-266.
128. Head RD, Smythe ML, Oprea TI, Walter CL, Green SM, Marshall GR. Validate - A new method for the receptor-based prediction of binding affinities of novel ligands. J Am Chem Soc 1996;118:3959-3969.
129. Faber K, Kowalski BR. Comment on a recent sensitivity analysis of radial base function and multilayer feedforward neural-network models. Chemometr Intell Lab 1996;34:293-297.
130. Lin SH, Zhang RS, Liu MC, Hu ZD. Neural-network - Topological indexes approach to the prediction of properties of alkene. J Chem Inf Comp Sci 1997;37:1146-1151.
131. Ruan R, Almaer S, Zou C, Chen PL. Spectrum analysis of mixing power curves for neural-network prediction of dough rheological properties. T ASAE 1997;40:677-681.
132. Fung CC, Wong KW, Eren H. Modular artificial neural-network for prediction of petrophysical properties from Well log data. IEEE T Instrum Meas 1997;46:1295-1299.
133. Zhang RS, Liu SH, Liu MC, Hu Z. Neural-network molecular descriptors approach to the prediction of properties of alkenes. Comput Chem 1997;21:335-341.
134. Salt DW, Yildiz N, Livingstone DJ, Tinsley CJ. The use of artificial neural networks in QSAR. Pestic Sci 1992;36:161-170.
135. Ichikawa H, Aoyama T. How to see characteristics of structural parameters in QSAR analysis: Descriptor mapping using neural networks. SAR and QSAR in Environ Res 1993;1:115-130.
136. Wiese M, Schaper KJ. Application of neural networks in the QSAR analysis of percent effect biological data: Comparison with adaptive least squares and nonlinear regression analysis. SAR and QSAR in Environ Res 1993;1:137-152.
137. Good AC, So SS, Richards WG. Structure-activity-relationships from molecular similarity-matrices. J Med Chem 1993;36:433-438.
138. Ajay. A unified framework for using neural networks to build QSARs. J Med Chem 1993;36:3565-3571.
139. Wikel JH, Dow ER. The use of neural networks for variable selection in QSAR. Bioorg Med Chem Lett 1993;3:645-651.
140. Ghoshal N, Mukhopadhayay SN, Ghoshal TK, Achari B. Quantitative structure-activity relationship studies using artificial neural networks. Indian J Chem B 1993;32:1045
141. Campbell JLE, Johnson KE. Abductive networks - Generalization, pattern-recognition, and prediction of chemical behavior. Can J Chem 1993;71:1800-1804.
142. Xu L, Ball JW, Dixon SL, Jurs PC. Quantitative structure-activity-relationships for toxicity of phenols using regression-analysis and computational neural networks. Environ Toxicol Chem 1994;13:841-851.
143. Bienfait B. Applications of high-resolution self-organizing maps to retrosynthetic and QSAR analysis. J Chem Inf Comp Sci 1994;34:890-898.
144. Manallack DT, Livingstone DJ. Limitations of functional-link nets as applied to QSAR data-analysis. Quant Struct Act Rel 1994;13:18-21.
145. Andrea TA. Novel structure-activity insights from neural-network models. ACS Sym Ser 1995;606;282-287.
146. Benigni R, Richard AM. QSARs of mutagens and carcinogens - 2 Case-studies illustrating problems in the construction of models for noncongeneric chemicals. Mutat Res 1996;371:29-46.
147. Barratt MD, Dixit MB, Jones PA. The use of in-vitro cytotoxicity measurements in QSAR methods for the prediction of the skin corrosivity potential of acids. Toxicol In Vitro 1996;10:283-290.
148. Tetko IV, Villa AE, Livingstone DJ. Neural network studies. 2. Variable selection. J Chem Inf Comp Sci 1996;36:794-803.
149. Tang Y, Jiang HL, Chen KX, Ji RY. QSAR study of artemisinin (Qinghaosu) derivatives using neural-network method. Indian J Chem B 1996;35:325-332.
150. Devillers J. Designing molecules with specific properties from intercommunicating hybrid systems. J Chem Inf Comp Sci 1996;36:1061-1066.
151. So SS, Karplus M. Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. J Med Chem 1996;39:1521-1530.
152. Domine D, Devillers J, Wienke D, Luydens L. Test series selection from nonlinear neural mapping. Quant Struct-Act Rel 1996;15:395-402.
153. Hall LH, Story CT. Boiling-point and critical-temperature of a heterogeneous data set - QSAR with atom type electrotopological state indexes using artificial neural networks. J Chem Inf Comp Sci 1996;36:1004-1014.
154. Hirst JD. Nonlinear quantitative structure-activity relationship for the inhibition of dihydrofolate-reductase by pyrimidines. J Med Chem 1996;39:3526-3532.
155. Beck B, Glen RC, Clark T. The inhibition of alpha-chymotrypsin predicted using theoretically derived molecular-properties. J Mol Graphics 1996;14:130.
156. Kyngas J, Valjakka J. Evolutionary neural networks in quantitative structure-activity-relationships of dihydrofolate-reductase inhibitors. Quant Struct Act Rel 1996;15:296-301.
157. So SS, Karplus M. Genetic neural networks for quantitative structure-activity relationships: Improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors. J Med Chem 1996;39:5246-5256.
158. So SS, Karplus M. Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations. J Med Chem 1997;40:4347-4359.
159. So SS, Karplus M. Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications. J Med Chem 1997;40:4360-4371.
160. Ghoshal N, Achari B, Ghoshal TK. A QSAR study of antiplatelet agents using artificial neural-network correlation with micelle-water partition-coefficient. Bioorg Med Chem Lett 1997;7:877-880.
161. Duprat AF, Huynh T, Dreyfus G. Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of logP. J Chem Inf Comp Sci 1998;38:586-594.
162. Aoyama T, Suzuki Y, Ichikawa H. Neural networks applied to quantitative structure-activity relationship analysis. J Med Chem 1990;33:2583-2590.
163. Aoyama T, Ichikawa H. Neural networks applied to pharmaceutical problems. 4. Basic operating characteristics of neural networks when applied to structure activity studies. Chem Pharm Bull Tokyo 1991;39:358-366.
164. Aoyama T, Ichikawa H. Neural networks applied to pharmaceutical problems. 5. Obtaining the correlation indexes between drug activity and structural parameters using a neural network. Chem Pharm Bull Tokyo 1991;39:372-378.
165. Andrea TA, Kalayeh H. Applications of neural networks in quantitative structure activity relationships of dihydrofolate reductase inhibitors. J Med Chem 1991;34:2834-2836.
166. Richards WG. Computer aided drug discovery. Proc Roy Soc Edinburgh Sect B 1992;99:105-111.
167. Weinstein JN, Kohn KW, Grever MR, Viswanadhan VN, Rubinstein LV, Monks AP, Scudiero DA, Welch L, Koutsoukos AD, Chiausa AJ, Paull KD. Neural computing in cancer drug development. Predicting mechanism of action. Science 1992;258:447-451.
168. Tetko IV, Luik AI, Poda GI. Applications of neural networks in structure-activity-relationships of a small number of molecules. J Med Chem 1993;36:811-814.
169. Sternberg MJE, Muggleton S, Shrinivasan A. Drug design by machine learning. Abstr Am Chem Soc 1995;209:165.
170. Hatrik S, Zahradnik E. Neural-network approach to the prediction of the toxicity of benzothiazolium salts from molecular-structure. J Chem Inf Comp Sci 1996;36:992-995.
171. Walczak B, Wegscheider W. Nonlinear modeling of chemical-data by combinations of linear and neuralnet methods. Anal Chim Acta 1993;283:508-517.
172. Ghoshal N, Mukhopadhyay SN, Ghoshal TK, Achari B. Quantitative structure-activity relationship studies of aromatic and heteroaromatic nitro-compounds using neural network. Bioorg Med Chem Lett 1993;3:329-332.
173. Song XH, Yu RQ. Artificial neural networks applied to the quantitative structure-activity relationship study of dihydropteridine reductase inhibitors. Chemometr Intell Lab 1993;19:101-109.
174. Tabak HH, Govind R. Prediction of biodegradation kinetics using a nonlinear group contribution method. Environ Toxicol Chem 1993;12:251-260.
175. Reibnegger G, Wernerfelmayer G, Wachter G. A note on the low-dimensional display of multivariate data using neural networks. J Mol Graphics 1993;11:129-133.
176. Jakus V. Artificial-intelligence in chemistry. Collect Czech Chem C 1992;57:2413-2451.
177. Adler B, Eitner B, Winterstein M. Simulation of the prediction of inhibitoric and cancerigenic properties of arylamines. Chem Tech Leipzig 1993;45:94-98.
178. Schuurmann G, Muller E. Backpropagation neural networks - Recognition vs prediction capability. Environ Toxicol Chem 1994;13:743-747.
179. Chastrette M, Zakarya D, Peyraud JF. Structure musk odor relationships for tetralins and indans using neural networks (on the contribution of descriptors to the classification). Eur J Med Chem 1994;29:343-348.
180. Cherqaoui D, Villemin D. Use of a neural-network to determine the boiling-point of alkanes. J Chem Soc Faraday T 1994:90:97-102.
181. Song XH, Chen Z, Yu, RQ. Artificial neural networks applied to the quantitative structure-activity relationship study of parasubstituted phenols. Sci China Ser B 1993;36:1443-1450.
182. Weinstein JN, Myers T, Buolamwini J, Raghavan K, van Osdol W, Licht J, Viswanadhan VN, Kohn KW, Rubinstein LV, Koutsoukos AD, Monks A, Scudiero DA, Anderson NL, Zaharevitz D, Chabner BA, Grever MR, Paull KD. Predictive statistics and artificial-intelligence in the US-National-Cancer-Institutes-Drug-Discovery-Program-for-Cancer-and-AIDS. Stem Cells 1994;12:13-22.
183. Hasegawa K, Deushi T, Yaegashi O, Miyashita Y, Sasaki S. Artificial neural-network studies in quantitative structure-activity-relationships of antifungal azoxy compounds. Eur J Med Chem 1995;30:569-574.
184. Kranz H, Vill V, Meyer B. Prediction of material properties from chemical structures - The clearing temperature of nematic liquid-crystals derived from their chemical structures by artificial neural networks. J Chem Inf Comp Sci 1996;36:1173-1177.
185. Tetteh J, Metcalfe E, Howells SL. Optimization of radial basis and backpropagation neural networks for modeling auto-ignition temperature by quantitative-structure property relationships. Chemometr Intell Lab 1996;32:177-191.
186. Gao C, Govind R, Tabak HH. Predicting soil sorption coefficients of organic-chemicals using a neural-network model. Environ Toxicol Chem 1996;15:1089-1096.
187. Isu Y, Nagashima U, Aoyama T, Hosoya H. Development of neural-network simulator for structure-activity correlation of molecules (Neco) - Prediction of Endo/Exo substitution of Norbornane derivatives and of carcinogenic activity of PAHs from C-13-NMR shifts. J Chem Inf Comp Sci 1996;36:286-293.
188. Jiang JH, Wang JH, Song XH, Yu RQ. Network training and architecture optimization by a recursive approach and a modified genetic algorithm. J Chemometr 1996;10:253-267.
189. King RD, Muggleton SH, Srinivasan A, Sternberg MJE. Structure-activity-relationships derived by machine learning - The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. P Natl Acad Sci USA 1996;93:438-442.
190. Barratt MD. Quantitative structure-activity-relationships (QSARs) for skin corrosivity of organic-acids, bases and phenols - Principal components and neural-network analysis of extended datasets. Toxicol In Vitro 1996;10:85-94.
191. Novic M, Nikolovskacoleska Z, Solmajer T. Quantitative structure-activity relationship of flavonoid P56(Lck) protein-tyrosine kinase inhibitors - Neural-network approach. J Chem Inf Comp Sci 1997; 37:990-998.
192. Ramos-Nino ME, Ramirez-Rodriguez CA, Clifford MN, Adams MR. A comparison
193. Fetisov VI, Kolesova VA, Kotov AV, Lisyanskii II, Chimishkyan AL, Martynov IV. Synthesis, antifungal activity and neural-network modeling of quantitative structure-activity-relationships for 4-phosphorylated derivatives of allophanic acid. Dokl Akad Nauk 1998;358:210-214
194. Ramos-Nino ME, Ramirez-Rodriguez CA, Clifford MN, Adams MR. QSARs for the effect of benzaldehydes on foodborne bacteria and the role of sulfhydryl groups as targets of their antibacterial activity. J Appl Microbiol 1998;84:207-212
195. Epa VC. Modeling the paramyxovirus hemagglutinin-neuraminidase protein. Proteins 1997;29:264-281.
196. Zakarya D, Larfaoui EM, Boulaamail A, Lakhlifi T. Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network. SAR and QSAR in Environ Res 1996;5:269-279.
197. Lukashin AV, Amirikian BR, Georgopoulos AP. Neural computations underlying the exertion of force: A model. Biol Cybern 1996;74:469-478.
198. Chow H, Chen H, Ng T, Myrdal P, Yalkowsky SH. Using backpropagation networks for the estimation of aqueous activity coefficients of aromatic organic compounds. J Chem Inf Comp Sci 1995;35:723-728.
199. van Osdol WW, Myers TG, Paull KD, Kohn KW, Weinstein JN. Use of the Kohonen self-organizing map to study the mechanisms of action of chemotherapeutic-agents. J Natl Cancer 1994;86:1853-1859.
200. So SS, Richards WG. Application of neural networks: Quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors. J Med Chem 1992; 35:3201-3207.
201. Hussain AS, Shivanand P, Johnson RD. Application of neural computing in pharmaceutical product development - Computer aided formulation design. Drug Dev Indust Pharm 1994;20:1739-1752.
202. Turkoglu M, Ozarslan R, Sakr A. Artificial neural network analysis of a direct compression tabletting study. Eur J Pharm Biopharm 1995;41:315-322.
203. Colbourn EA, Rowe EC. Modelling and optimization of a tablet formulation using neural networks and genetic algorithms. Pharm Techn Eur 1996;8:46-55.
204. Bums JA, Whitesides GM. Feedforward neural networks in chemistry - Mathematical systems for classification and pattern-recognition. Chem Rev 1993;93:2583-2601.
205. Kröse B, van der Smagt P. An introduction to neural networks. http://www.fwi.uva.nl/researcheuro. 1997.
206. Zupan J, Gasteiger J. Neural networks for chemists. Springer-Verlag; 1995.
207. Masters T. Practical neural network recipes in C++. Boston: Academic; 1996.
208. Sharpe PK, Caleb P. Artificial neural networks within medical decision support systems. Scand J Clin Lab Inv 1994;219:3-11.
209. Place JF, Truchaud A, Ozawa K, Pardue H, Schnipelsky P. International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems. Clin Chim Acta 1994;231:S5-34.
210. Winkel P. Artificial-intelligence within the chemical laboratory. Ann Biol Clin 1994;52:277-282.
211. Reibnegger G, Weiss G, Werner-Felmayer G, Judmaier G, Wachter H. Neural networks as a tool for utilizing laboratory information - Comparison with linear discriminant-analysis and with classification and regression trees. P Natl Acad Sci USA 1991;88:1426-1430.
212. Livingstone DJ, Hesketh G, Clayworth D. Novel method for the display of multivariate data using neural networks. J Mol Graphics 1991;9:115-118.
213. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neur Net 1989;2:359-366.
214. Hartman E, Keeler JD, Kowalski JM. Layered neural networks with Gaussian hidden units as universal approximations. Neur Comput 1990;2:210-215.
215. Yu XH. Can backpropagation error surface not have local minima? IEEE Trans Neur Net 1992;3:1019-1021.
216. HyperChem®, Ver 4.5, Canada: HypecCube Inc.; 1997.
217. ISIS/Base®, Ver. 1.2, San Leandro, CA: MDL Information Systems Inc.; 1996.
218. 3DNET, Ver. 1.0, Budapest, Hungary: CompElit Ltd.; 1998.
219. Kalman BL, Kwasny SC. International Conference on Neural Networks. Baltimore: 1992.
220. Sussman HJ. Uniqueness of the weights for minimal feedforward nets with a given input-output map. Neural Networks 1992;5:589-593.
221. Connolly ML. Computation of molecular volume. J Am Chem Soc 1985;107:1118-1124.
222. Iwase K, Komatsu K, Hirono S, Nakagawa S, Moriguchi I. Estimation of hydrophobicity based on the solvent accessible surface-area of molecules. Chem Pharm Bull 1985;33:2114-2121.
223. De Bruijn J, Hermens J. Relationships between octanol water partition-coefficients and total molecular-surface area and total molecular volume of hydrophobic organic-chemicals. Quant Struct-Act Rel 1990;9:11-21.
224. Mortier WJ, van Genechten K, Gasteiger J. Electronegativity equalization - Application and parametrization. J Am Chem Soc 1985;107:829-835.
225. Andrews PR, Craik DJ, Martin JL. Functional-group contributions to drug receptor interactions. J Med Chem 1984;27:1648-1657.
226. Meyer AY. The size of molecules. J Chem Soc Rev 1986;15:449-474.
227. Gaillard P, Carrupt PA, Testa B, Boudon A. Molecular lipophilicity potential, a tool in 3D QSAR - Method and applications. J Comput-Aid Mol Des 1994;8:83-86.
228. Miller KJ. Additivity methods in molecular polarizability. J Am Chem Soc 1990; 112:8533-8542.
229. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occuring nucleoside antibiotics. J Chem Inf Comput Sci 1989;29:163-172.