Computational neural network

Zheng E, Zheng G, Deaciuc AG Zhan CG Dwoskin LP, Crooks PA. (2007) Computational neural network analysis of the affinity of lobeline and tetra-benazine analogs for the vesicnlar monoamine transporter-2. Bioorg Med Chem 15 2975-2992. 

Mattioni BE, Jurs PC (2002) Prediction of glass transition temperatures from monomer and repeat unit structure using computational neural networks. J Chem Inf Comput Sci 42 232-240... 

Ulmer II CW, Smith DA, Sumpter BG et al. (1998) Computational neural networks and the rational design of polymeric materials the next generation polycarbonates. Comput Theor Polym Sci 8 311-321... 

L. I. Nord and S. P. Jacobsson, A novel method for examination of the variable contribution to computational neural network models, Chemom. Intel . Lab. Syst., 44(1-2), 1998, 153-160. 

Gakh, A.A. Sumpter, B.G. Noid, D.W. Sachleben, R.A. Moyer, B.A. Prediction of complexation properties of crown ethers using computational neural networks. J. Inclusion Phenom. Mol. Recognit. Chem. 1997, 27 (3), 201-213. 

Guha R, Stanton DT, Jurs PC (2005) Interpreting computational neural network QSAR models a detailed interpretation of the weights and biases. J Chem Inf Model 45 1109-1121... 

Environment Canada Fathead minnow acute toxicity ECOSAR, TOPKAT, probabilistic neural network, computational neural network, ASTER, OASIS Moore et al. (2003)... 

Pressures of Hydrocarbons and Halohydrocarbons from Molecular Structure with a Computation Neural Network. 

Goll, E.S. and Jurs, PC. (1999a). Prediction of the Normal Boiling Points of Organic Compounds from Molecular Structures with a Computational Neural Network Model. J.Chem.Inf.Com-put.Sci., 39,914-983. 

Maggiora, G.M., Elrod, D.W and Trenary, R.G. (1992). Computational Neural Networks as Model Free Mapping Devices. J.Chem.Inf.Comput.ScL, 32,732-741. 

Wessel, M.D. and Jurs, PC. (1994). Prediction of Reduced Ion Mobility Constants from Structural Information Using Multiple Linear Regression Analysis and Computational Neural Networks. Anal.Chem., 66, 2480-2487. 

Models can be generated using stepwise addition multiple linear regression as the descriptor selection criterion. Leaps-and-bounds regression [10] and simulated annealing (ANNUN) can be used to find a subset of descriptors that yield a statistically sound model. The best descriptor subset found with multiple linear regression can also be used to build a computational neural network model. The root mean square (rms) errors and the predictive power of the neural network model are usually improved due to the higher number of adjustable parameters and nonlinear behavior of the computational neural network model. 

The multiple linear regression models are validated using standard statistical techniques. These techniques include inspection of residual plots, standard deviation, and multiple correlation coefficient. Both regression and computational neural network models are validated using external prediction. The prediction set is not used for descriptor selection, descriptor reduction, or model development, and it therefore represents a true unknown data set. In order to ascertain the predictive power of a model the rms error is computed for the prediction set. 

Multiple linear regression has been shown to adequately relate the structure of a compound to a biological activity of interest. Therefore, linear regression has been used extensively in QSARs. Recently, however, research has shown that computational neural networks can lead to better QSAR models [13-16]. As with linear regression, optimization opportunities arise for computational neural networks using SA or GSA. 

Neural networks were originally designed as a model for the activity of the human brain. However, a computational neural network can be thought of simply as a nonlinear regression model when applied to QSAR studies. The computational neural network is a... 

Compound used in the cross-validation set for computational neural networks. Compound used in the prediction set for regression and neural network models. 

The five descriptors found in this model were then fed to a computational neural network in an attempt to improve the predictive ability. The program ANN was used to optimize the starting weights and biases. The quality of the model was assessed by calculating the residuals [actual-predicted values of -logCLCso)] of the prediction set compounds. 

Figure 5. Calculated vs. observed -log( 5o) values using a computational neural network model with the descriptor subset selected by generalized simulated annealing (anndes).

Goll, E.S. and Jurs, P.C. (1999a) Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model./. Chem. Inf. Comput. Sci., 39,974— 983. 

Guha, R., Stanton, D.T and Jurs, P.C. (2005) Interpreting computational neural network quantitative structure-activity relationship models a detailed interpretation ofthe weights and biases./. Chem. Inf. Model., 45, 1109-1121. 

Quantitative structure-property relationships for colour reagents and their colour reactions with ytterbium using regression analysis and computational neural networks. Anal. Chim. Acta, 321, 97-103. 

Mattioni, B.E. and Jurs, P.C. (2003) Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis. /. Mol. Graph. Model., 21, 391-419. 

Serra, J.R., Jurs, P.C. and Kaiser, K.L.E. (2001) Linear regression and computational neural network prediction of Tetrahymena acute toxicity for aromatic compounds from molecular structure. Chem. Res. Toxicol., 14, 1535—1545. 

S.R. Gallant, S.P. Fraleigh and S.M. Cramer, Deconvolution of Overlapping Chromatographic Peaks using a Cerebellar Model Arithmetic Computer Neural Network, Chemometrics Intelligent Laboratory Systems 18 (1993), 41-57. 

Key words expert system, evolntionary computation, neural network,... 

Sumpter BG, Noid DW (1996) On the Design, Analysis, and Characterization of Materials Using Computational Neural Networks, Ann Rev Mater Sci 26 223-277. 

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