How to see characteristics of structural parameters in QSAR analysis: descriptor mapping using neural networks.

In addition to its outstanding abilities in both classification and fitting, the neural network can also accurately predict the values of the untrained region. To rationalize this ability of prediction, the authors mathematically discussed the valid region of prediction. Based on such a background, the authors proposed "descriptor mapping" in the QSAR analysis, which visualizes the nonlinear dependencies between structural parameters. A variable of the linear multiple regression analysis in the QSAR study is supposed to be linear to the biological intensity and is independent of other variables. Analysis by the descriptor mapping method discloses the reality.

[1]  H Ichikawa,et al.  Neural networks applied to quantitative structure-activity relationship analysis. , 1990, Journal of medicinal chemistry.

[2]  C. Hansch,et al.  On the structure of medicinal chemistry. , 1976, Journal of medicinal chemistry.

[3]  E. Kutter,et al.  Practical Procedures in Drug Design , 1980 .

[4]  Tomoo Aoyama,et al.  Reconstruction of Weight Matrices in Neural Networks : A Method of Correlating Outputs with Inputs , 1991 .

[5]  Tomoo Aoyama,et al.  Obtaining the correlation indices between drug activity and structural parameters using a neural network , 1991 .

[6]  J. Topliss.  Quantitative structure-activity relationships of drugs , 1983 .

[7]  H Ichikawa,et al.  Neural networks applied to structure-activity relationships. , 1990, Journal of medicinal chemistry.

[8]  Khan Ah,et al.  Potential central nervous system antitumor agents. Aziridinylbenzoquinones. 1. , 1976 .

[9]  Tomoo Aoyama,et al.  Basic Operating Characteristics of Neural Networks When Applied to Structure-Activity Studies , 1991 .

[10]  Hiroshi Ichikawa,et al.  Neural networks as nonlinear structure-activity relationship analyzers. Useful functions of the partial derivative method in multilayer neural networks , 1992, J. Chem. Inf. Comput. Sci..

[11]  Toshio Fujita,et al.  The Correlation of Biological Activity of Plant Growth Regulators and Chloromycetin Derivatives with Hammett Constants and Partition Coefficients , 1963 .

[12]  T. A. Andrea,et al.  Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. , 1991, Journal of medicinal chemistry.

[13]  M. Arakawa,et al.  Antileukemic Agents. I. Some 2, 5-Disubstituted p-Benzoquinones , 1972 .

[14]  C. Hansch,et al.  p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , 1964 .

[15]  H Ichikawa,et al.  Neural networks applied to pharmaceutical problems. I. Method and application to decision making. , 1989, Chemical & pharmaceutical bulletin.

[16]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[17]  Hideo Nakao,et al.  Antileukemic Agents. II. New 2, 5-Bis (1-aziridinyl)-p-benzoquinone Derivatives , 1972 .

[18]  M. Arakawa,et al.  Quantitative structure-activity relationships in 2,5-bis(1-aziridinyl)-p-benzoquinone derivatives against leukemia L-1210. , 1979, Journal of medicinal chemistry.