Quantitative structure-activity neural network applications

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. 

So, S.-S. and Karplus, M. (1996a). Evolutionary Optimization in Quantitative Structure-Activity Relationship An Application of Genetic Neural Networks. J.Med.Chem.,39,1521-1530. 

Quantitative Structure-Activity Relationship An Application of Genetic Neural Networks. 

T A and H Kalayeh 1991. Applications of Neural Networks in Quantitative Structure-Activity ationships of Dihydrofolate Reductase Inhibitors, journal of Medicinal Chemistry 34 2824-2836. ik M and R C Glen 1992. Applications of Rule-induction in the Derivation of Quantitative icture-Activity Relationships. Journal of Computer-Aided Molecular Design 6 349-383. 

TA Andrea, H Kalayeh. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 34 2824-2836, 1991. 

Maddalena DJ. Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin Ther Patents 1996 6 239-51. 

So, S.S. and 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. 

So, S.S. Kaeplus, M. Genetic neural networks for quantitative structure-activity relationships improvements and application of benzodiazepine afBnity for benzodiazepine/GABAA receptors./. Med. Chem. 1996, 39, 5246-5256. 

Andrea, T.A. and Kalayeh, H. (1991). Applications of Neural Networks in Quantitative Structure-Activity Relationships of Dihydropholate Reductase Inhibitors. JMetLChem., 34, 2824-2836. 

Ivanciuc, O. (1996). Artificial Neural Networks Applications. 2. Using Theoretical Descriptors of Molecular Structure in Quantitative Structure-Activity Relationships Analysis of the Inhibition of Dihydrofolate Reductase. Rev.Roum.Chim., 41,645-652. 

Polanski, J., Gasteiger, J., Wagener, M. and Sadowski, J. (1998). The Comparison of Molecular Surfaces by Neural Networks and its Applications to Quantitative Structure Activity Studies. QuanlStruct.-AcuRelat., 17, 27-36. 

So, S.-S. and Richards, W.G. (1992). 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., 35,3201-3207. 

In terms of practical application, expert systems overlap with systems for deriving and applying quantitative structure-activity relationship (QSAR) models or equations, and with systems using artificial neural networks (ANN) or genetic algorithms. The expert systems described in this chapter are characterized by their use of a generalized store of knowledge. 

Borowski, T., Krol, M., Broclawik, E., Baranowski, T, Strekowski, L. and Mokrosz, M.J. (2000) Application of similarity matrices and genetic neural networks in quantitative structure-activity relationships of 2- or 4-(4-methylpiperazino) pyrimidines 5-HT2A receptor antagonists./. Med. Chem., 43, 1901-1909. 

Ivanciuc, O. (1997) Artificial neural networks applications. Part 3. A quantitative structure-activity relationship for the actinidin hydrolysis of substituted-phenyl hippurates. Rev. Roum. Chim., 42, 325-332. 

Andrea, T. A. Kalayeh, H. (1991). Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. Journal of Medicinal Chemistry. Vol. 34, pp. 2824-2836. ISSN 0022-2623 Aparido, R. Aparicio-Ruiz, R. (2002). Chemometrics as an aid in authentication. In Oils and Fats Authentication, M Jee (Ed.), 156-180, Blackwell Publishing and CRC Press, ISBN 1841273309, Oxford, UK and FL, USA Bishop, CM. (2000). Neural Networks for Pattern Recognition, Oxford University Press, ISBN 0198538642, NY, USA... 

Before reviewing the existing applications of neural networks to combinatorial discovery, we offer brief descriptions of the key concepts in quantitative structure-activity relationships (QSAR), neural networks, and virtual high-throughput screening (VHTS). There are a substantial number of reviews of the applications of QSAR to chemistry and dmg design (2-13) and of applications of neural networks to chemistry (13-29), to which the reader is referred for more detailed discussions of these topics. 

Sutter and co-workers reported a method for automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing (36,132). The cost function used to evaluate the effectiveness of the deseriptors was based on a neural network. The result is an automated descriptor selection algorithm that is an optimization inside of an optimization. Application of the method to QSAR shows that effective descriptor subsets are found, and they support models that are as good or better than those obtained using traditional linear regression methods. 

Hansch, C. and Fujita, T. (1964) p-o-7i Analysis. A method for correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86,1616. Maddalena, D. J. (1996) Applications of artificial neural networks to quantitative structure-activity relationships. Expert Opin. Ther. Pat. 6,239-251. 

Balaban, A. T. and Basak, S. C. (2000) Trends and possibilities for future developments of topological indices. Abstr. Pap. Am. Chem. Soc. 220th, COMP-048. Sutter, J. M. and Jurs, P. C. (1995) Selection of molecular descriptors for quantitative structure-activity relationships. Data//anrfZ. Sci. Technol. 15, 111-132. Winkler, D. A. and Burden, F. R. (2000) Robust QSAR models from novel descriptors and Bayesian regularized neural networks. Mol. Simul. 24,243-258. Maddalena, D. J. (1998) Applications of soft computing in drug design. Expert Opin. Ther. Pat. 8, 249-258. 

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