Applications of soft computing in drug design

Bibliography

• ZADEH LA: The roles of fuzzy logic and soft computing in the conception, design and deployment of intelli-gent systems. BT Technol. J. (1996) 14:32–36.
   Web of Science ®Google Scholar
• AZVINE B, AZARMI N, TSUI KC: Soft computing - a tool for building intelligent systems. BT Technol. J. (1996) 14:37–45.
   Google Scholar
• RUMMELHART DE, MCCLELLAND JL: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press. Cambridge (1986) Volumes 1 & 2.
   Google Scholar
• MADDALENA DJ: Applications of artificial neural net-works to quantitative structure-activity relationships. Exp. Opin. Ther. Patents (1996) 6:239–251.
   Web of Science ®Google Scholar
• DEVILLERS J: Principals of QSAR and Drug Design. Vol 2 Neural Networks in QSAR and Drug Design. Academic Press, San Diego (1996).
   Google Scholar
• VENTURA S, SILVA M, KREZ-BENDITO D et al: Computa-tional neural networks in conjunction with principal component analysis for resolving highly non-linear kinetics. J. Chem. Info. Comput. Sci. (1997) 37:287–291.
   Google Scholar
• CHEN D, CHEN Y, HU S: A pattern classification proce-dure the multivariate statistical analysis with neural networks. Comput. Chem. (1997) 21:109–113.
   PubMedGoogle Scholar
• NEFATI H, CENCE J-M, LEGENDRE J-J: Prediction of the impact sensitivity by neural networks. J. Chem. Info. Comput. Sci. (1996) 36:804–810.
   Google Scholar
• SO S-S, KARPLUS M: Genetic neural networks for quan-titative structure-activity relationships: improvements and applications of benzodiazepine affinity for benzo-diazepine/GABAA receptors. J. Med. Chem. (1996) 39:5246–5256.
   PubMedGoogle Scholar
• •Good solid experimental paper illustrating genetic neural nets.
   Google Scholar
• MADDALENA DJ, JOHNSTON GAR: Prediction of receptor properties and binding affinity of ligands to benzo-diazepine/GABAA receptors using artificial neural networks. J. Med. Chem. (1995) 38:715–724.
   PubMed Web of Science ®Google Scholar
• TETKO IV, VILLA AE, LIVINGSTONE DJ: Neural network studies. 2. Variable selection. J. Chem. Info. Comput. (1996) 36:794–803.
   PubMedGoogle Scholar
• HOSSEINI M, MADDALENA DJ, SPENCE I: Using artificial neural networks to classify the activity of capsaicin and its analogues. J. Chem. Info. Comput. Sci. (1997) 37:1129–1137.
   PubMedGoogle Scholar
• KIREEV DB: ChemNet: a novel neural network basedmethod for graph/property mapping. J. Chem. Info. Comput. Sci. (1995) 35:175–180.
   Google Scholar
• BASKIN II: A neural device for the direct correlations between structures and properties of chemical com-pounds. J. Chem. Info. Comput. ScL (1997) 37:715–721.
   Google Scholar
• SONG X-H, XIAO M, YU R-Q: Artificial neural networks applied to classification of mutagenic activity of nitro-substituted polycyclic aromatic hydrocarbons. Com-put. Chem. (1994) 18:391–396.
   PubMedGoogle Scholar
• HATRIX, ZAHRADN1K P: Neural network approach to the prediction of the toxicity of benzothiazolium salts from molecular structure. J. Chem. Info. Comput. Sci. (1996) 36:992–995.
   PubMedGoogle Scholar
• VRACKO M: A study of structure-carcinogenic potency relationship with artificial neural networks. The using of descriptors related to geometrical and electronic structures. J. Chem. Info. Comput. Sci. (1997) 37:1037–1043.
   Google Scholar
• ISU Y, NAGASHIMA U, AOYAMA T et al.: Development of neural network simulator for structure-activity corre-lation of molecules (NECO). Prediction of endo/exo substitution of norbornane derivatives and carcino-genic activity of PAHs from 13C-NMR shifts. J. Chem. Info. Comput. Sci. (1996) 36:286–293.
   Google Scholar
• TANGY, WANG H-W, CHEN K-X eta].: QSAR of 3-methyl fentanyl derivatives studied with neural networks method. Acta Pharm. Sinica (1995) 16:26–32.
   Google Scholar
• ADAMS H-P, KOZIOL JA: Prediction of binding to MHC class I molecules. J. Immunol. Meth. (1995) 185:181–190.
   PubMedGoogle Scholar
• NOVIC M, NIKOLOVSKA-COLESKA Z, SOLMAJER T: Quan-titative structure-activity relationship of flavenoid p561ck protein tyrosine kinase inhibitors. A neural network approach. J. Chem. Info. Comput. Sci. (1997) 37:990–998.
   Google Scholar
• CHASTRETTE M, CRETIN D, EL AIM C: Structure-odor re-lationships: using neural networks in the estimation of camphorous or fruity odors and olfactory thresh-olds of aliphatic alcohols. J. Chem. Info. Comput. (1996) 36:108–113.
   PubMedGoogle Scholar
• BURDEN FR: Using artificial neural networks to predict biological activity from simple molecular structural considerations. Quant Strucl-Act. Relat. (1996) 15:7–11.
   Google Scholar
• SVOZIL D, SEVCIK JG: Neural network prediction of the solvatochromic polarity/polarizability parameter icH2. J. Chem. Info. Comput. Sci. (1997) 37:338–342.
   Google Scholar
• HUUSKONEN J, TASKINEN J: Neural network modelling for estimation of the aqueous solubility of structurally related drugs. J. Pharm. Sci. (1997) 86:450–454.
   PubMed Web of Science ®Google Scholar
• SMITS JRM, MELSSEN WJ, DAALMANS GJ et al.: Using mo-lecular representations in combination with neural networks. A case study: prediction of the HPLC reten-tion index. Comput. Chem. (1994) 18:157–172.
   Google Scholar
• VENTURA S, SILVA M, PEREZ-BENDITO D: Estimation of parameters of kinetic compartmental models by use of computational neural networks. J. Chem. Info. Comput. Sci. (1997) 37:517–521.
   Google Scholar
• CHOY WY, SANCTUARY BC: Using neural network pre-dicted secondary structure information in automated protein NMR assignment. J. Chem. Info. Comput. ScL (1997) 37:1086–1094.
   PubMedGoogle Scholar
• EGHBALDAR A, FORREST TP, CABROL-BASS D etal.: Iden-tification of structural features from mass spectrome-try using a neural network approach: application to trimethylsilyl derivatives used for medical diagnosis. J. Chem. Info. Comput. Sci. (1996) 36:637–643.
   Google Scholar
• CUNDARI TR, MOODY EW: A comparison of neural net-work versus quantum mechanics for inorganic sys-tems. J. Chem. Info. Comput. Sci. (1997) 37:871–875.
   Google Scholar
• MUNK ME, MADISON MS, ROBB EW: The neural networkas a tool for multispectral interpretation. J. Chem. Info. Comput. Sci. (1996) 36:231–238.
   PubMedGoogle Scholar
• TAFEIT E, ESTELBERGER W, HOREJSI R et al. Neural net- works as a tool for compact representation of ab initio © Ashley Publications Ltd. All rights reserved.Exp. Opin. Ther. Patents (1998) 8(3) molecular potential energy surfaces. J. Mol Graphics. (1996) 14:12–18.
   Google Scholar
• •Interesting use of neural networks as a storage vessel for complex data.
   Google Scholar
• DOMINE D, DEVILLERS J, WIENKE D et al.: ART-2A for op-timal test series design in QSAR. J. Chem. Info. Comput. Sci. (1997) 37:10–17.
   Google Scholar
• KOHONEN T: Self Organising Maps (2nd Edition). Springer (1997).
   Google Scholar
• ••Almost everything you wanted to know about Kohonen netsbut never thought to ask.
   Google Scholar
• KOCJANCIC R, ZUPAN J: Application of a feed forward artificial neural network as a mapping device. J. Chem. Info. Comput. Sci. (1997) 37:985–989.
   Google Scholar
• LIVINGSTONE DJ, HESKETH G, CLAYWORTH D: Novel method for the display of multivariate data using neu-ral networks. J. Mol. Graphics (1991) 9:115–118.
   PubMedGoogle Scholar
• POLANSKI J: Neural network for the simulation of mo-lecular recognition within the MS-Windows environ-ment. J. Chem. Info. Comput. ScL (1996) 36:694–705.
   Google Scholar
• POLANSKI J: The receptor-like neural network for mod-elling corticosteroid and testosterone binding globu-lins. J. Chem. Info. Comput. Sci. (1997) 37:553–561.
   PubMedGoogle Scholar
• ANZALI S, BARNICKEL G, KRUG M et al: The comparison of geometric and electrostatic properties of molecular surfaces by neural networks: Application to the analy-sis of corticosteroid-binding globulin activity of ster-oids. J. Comput.-Aided Mol. Des. (1996) 10:521–534.
   PubMed Web of Science ®Google Scholar
• JAIN AN, HARRIS NL, PARK JY: Quantitative binding site model generation: compass applied to multiple che-motypes targeting the 5-HTia receptor. J. Med Chem. (1995) 38:1295–1308.
   PubMedGoogle Scholar
• •Impressive practical use of soft computing in drug design.
   Google Scholar
• SWINGLER K: Applying Neural Networks. A Practical Guide. Academic Press, London (1996).
   Google Scholar
• ••Very useful book on almost every practical aspect of neuralnetworks.
   Google Scholar
• BURDEN FR: Mapping analytical functions using neural networks. J. Chem. Info. Comput. Sci. (1994) 34:1229–1231.
   Google Scholar
• WISE BM, HOLT BR, GALLAGHER NB et al.: A comparison of neural networks, non-linear biased regression and a genetic algorithm for dynamic model identification. Chemomet. Intel. Lab. Syst. (1995) 30:81–89.
   Web of Science ®Google Scholar
• DERKS EPP, BECKERS LM, MELSSEN WJ et al.: Parallel processing of chemical information in a local area net-work - II. A parallel cross-validation procedure for arti-ficial neural networks. Comput. Chem. (1996) 20:439–448.
   Google Scholar
• LEVIN M: Use of genetic algorithms to solve biomedical problems. MD Computing (1995) 12:193–199.
   PubMedGoogle Scholar
• WILLETT P: Genetic algorithms in molecular recogni-tion and design. Trends Biotechnol. (1995) 13:516–21.
   PubMed Web of Science ®Google Scholar
• CLARKE DE, WESTHEAD DR: Evolutionary algorithms in computer-aided molecular design. J. Comp.-Aided Mol. Des. (1996) 10:337–358.
   PubMedGoogle Scholar
• •Good review on evolutionary algorithms in drug design.
   Google Scholar
• DEVILLERS J: Genetic Algorithms in Molecular Modelling Academic Press, London (1996).
   Google Scholar
• MADDALENA DJ, SNOWDON GM: Applications of ge-netic algorithms to drug design. Exp. Opin. Ther. Patents (1997) 7:247–254.
   Web of Science ®Google Scholar
• ROGERS D, HOPFINGER AJ: Application of genetic func-tion approximation to quantitative structure-activity relationships and quantitative structure-property re-lationships. J. Chem. Info. Comput. Sci. (1994) 34:854–866.
   Google Scholar
• KUBINYI H: Evolutionary variable selection in regres-sion and PLS analyses. J. Chemomet. (1996) 10:119–133.
   Web of Science ®Google Scholar
• HASEGAWA K, MIYASHITA Y, FUNATSU K: GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. J. Chem. Info. Comput. Sci. (1997) 37:306–310.
   PubMedGoogle Scholar
• YOSHIDA H, FUNATSU K: Optimization of the inner re-lation function of QPLS using genetic algorithm. J. Chem. Info. Comput. Sci. (1997) 37:1115–1121.
   Google Scholar
• KYNGAS J, VALJAKKA J: Evolutionary neural networks in quantitative structure-activity relationships of dihy-drofolate reductase inhibitors. Quant. Struct-Activ. Re-lat. (1996) 15:296–301.
   Google Scholar
• PULLAN WJ: Structure prediction of benzene clusters using a genetic algorithm. J. Chem. Info. Comput. (1997) 37:1189–1193.
   Google Scholar
• PEDERSON JT, MOULT J: Genetic algorithms for protein structure prediction. Curr. Opin. Struct. Biol. (1996) 6:227–31.
   PubMedGoogle Scholar
• WESTHEAD DR, CLARK DE, FRENKEL D et al.: PRO LIGAND: an approach to de novo molecular design. 3 A genetic algorithm for structure refinement. J. Com-put-Aided Mol. Des. (1995) 9:139–148.
   Google Scholar
• JONES G, WILLETT P, GLEN RC: A genetic algorithm for flexible molecular overlay and pharmacophore eluci-dation. J. Comput.-Aided Mol. Des. (1995) 9:532–49.
   PubMed Web of Science ®Google Scholar
• •Interesting and potentially useful method for pharma-cophore design.
   Google Scholar
• THOMPSON LA, ELLMAN JA: Synthesis and applications of small molecule libraries. Chem. Rev. (1996) 96:555–600.
   PubMed Web of Science ®Google Scholar
• BROWN RD, MARTIN YC: Designing combinatorial li-brary mixtures using a genetic algorithm. J. Med Chem. (1997) 40:2304–2313.
   PubMed Web of Science ®Google Scholar
• SCHNEIDER G, SCHUCHHARDT J, WREDE P: Evolution-ary optimisation in multimodal search space. Biol. Cy-bern. (1996) 74 : 203–207.
   Google Scholar
• VENKATSUBRAMANIAN V, CHAN K, CURUTHERS JM: Evo-lutionary design of molecules with desired properties using the genetic algorithm. J. Chem. Info. Comput. Sci. (1995) 35:188–195.
   Google Scholar
• COX E: The Fuzzy Logic Handbook. Academic Press, Cam-bridge (1994).
   Google Scholar
• AKAY M, COHEN M, HUDSON D: Fuzzy sets in life sci-ences. Fuzzy Sets Syst. (1997) 90:219–224.
   Google Scholar
• Fuzzy Logic in Chemistry. Rouvray DH (Ed.), AcademicPress, New York (1997).
   Google Scholar
• HEIDEN W, BRICKMANN J: Segmentation of protein sur-faces using fuzzy logic. J. Mol. Graphics (1994) 12:106–115.
   PubMedGoogle Scholar
• MEZEY PG, WALKER PD: Fuzzy molecular fragments in drug research. Drug Res. Today (1997) 2:132–137.
   Google Scholar
• EXNER TE, BRICKMANN J: New docking algorithm based on fuzzy set theory. J. Mol. Model. (1997) 3:321–324.
   Google Scholar
• SPROULE BA, BAZOON M, SHULMAN KI etal.: Fuzzy logic pharmacokinetic modelling - applications to lithium concentration predictions. Clin. Pharmacol Ther. (1997) 62:29–40.
   Google Scholar
• VON NEUMAN J: Theory of Self-Reproducing Automata.Burks A (Ed.), University of Illinois Press, Urbana, Illinois (1966).
   Google Scholar
• ERMENTROUT GB, EDELSTEIN-KESHET L: Cellular auto-mata approaches to biological modelling. J. Theoret. Biol. (1993) 160:97–133.
   PubMed Web of Science ®Google Scholar
• •Good review of cellular automata although becoming dated.
   Google Scholar
• KIER LB, CHENG C-K: A cellular automata model of anaqueous solution. J. Chem. Info. Comput. ScL (1994) 34:1334–1337.
   Google Scholar
• KIER LB, CHENG C-K, TESTA B et al.: A cellular automatamodel of the hydrophobic effect. Pharm. Res. (1995) 12:615-620. KIER LB, CHENG C-K: A cellular automata model of sol-ute dissolution. Pharm. Res. (1995) 12:1521-1525. CHENG C-K, KIER LB: A cellular automata model of oil-water partitioning. J. Chem. Info. Comput. Sci. (1995) 35:1054–1059.
   Google Scholar
• KIER LB, CHENG C-K, TESTA B et al.: A cellular automata model of micelle formation. Pharm. Res. (1996) 13:1419–1422.
   PubMedGoogle Scholar
• SEYBOLD PG, KIER LB, CHENG C-K: Simulation of first order chemical kinetics using cellular automata. J. Chem. Info. Comput. Sci. (1996) 37:386–391.
   Google Scholar
• •This work indirectly suggests that the next use for cellular automata might be in receptor kinetics.
   Google Scholar
• KOCH HP: The concept of fractals in the pharmaceuti-cal sciences. Pharmazie (1993) 48:643–659.
   PubMedGoogle Scholar
• MACHERAS P, ARGYRAKIS P, POLYMILIS C: Fractal ge-ometry, fractal kinetics and chaos en route to biophar-maceutical sciences. Eur. J. Drug Metab. PharmacokineL (1996) 21:77–86.
   PubMedGoogle Scholar
• TALLARINDA RJ, FREEMAN K: Chaos and control in mass-action binding of endogenous compounds. Ann. Biomed. Engl. (1994) 22:153–161.
   PubMedGoogle Scholar
• GARFINKEL A, SPANO ML, DITTO WL et al.: Controlling cardiac chaos. Science (1992) 1230–1235.
   PubMedGoogle Scholar
• LIEBOVITCH LS, CZEGLEDY: A model of ion channel ki-netics based on deterministic, chaotic motion in a po-tential with two local minima. Ann. Biomed. Engl. (1992) 20:517–532.
   PubMedGoogle Scholar
• FARIN D, AVNIR D: Use of fractal geometry to determine effects on surface morphology on drug dissolution. J. Pharm. ScL (1992) 81:54–57.
   PubMed Web of Science ®Google Scholar
• CARSTENSEN JT, FRANCINI M: The use of fractal geome-try in pharmaceutical systems. Drug Dev. Ind. Pharm. (1993) 19:85–100.
   Web of Science ®Google Scholar
• BOWER C, WASINGTON C, PUREWAL TS: Fractal geome-try of drug aggregates in aerosol propellant suspen-sions. Intern. J. Pharm. (1995) 118:229–235.
   Web of Science ®Google Scholar
• GOETZE T, BRICKHMANN J: Self similarity of protein surfaces. Biophys. (1992) 61:109–118.
   PubMedGoogle Scholar
• KOCH HP, ZACEK HP: Fractals also in pharmacokinet-ics? Pharmazie (1991) 46:870–871.
   PubMedGoogle Scholar
• RAGAZZI E: Hidden fractals in pharmacodynamics. Pharmazie (1995) 50:66–68.
   PubMedGoogle Scholar
• SAVAGEAU MA: Mechaelis-Menten mechanism recon-sidered: implications of fractal kinetics. Pharm. Res. (1995) 12:541–548.
   PubMedGoogle Scholar
• LIEBOVITCH LS, TOTH TI : Using fractals to understand the opening and closing of ion channels. Ann. Biomed. Engl. (1990) 18:177-194. Desmond J Maddalena
   Google Scholar