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Evolutionary neural networks in quantitative structure - activity relationships of dihydrofolate reductase inhibitors (1996).
| Content Provider | CiteSeerX |
|---|---|
| Author | Kyngäs, Jari Valjakka, Jarkko |
| Abstract | The evolutionary neural network (ENN) is a new system for modeling multifactor data. The strengths of ENN's are that they can extract insignificant predictors, choose the size of the hidden layer and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Dihydrofolate Reductase Inhibitors. As a result, we found that evolutionary neural networks give more accurate predictions than statistical methods and feedforward neural networks. Keywords: Evolutionary neural netwoks, Genetic algorithms, QSAR. 3 1. Introduction The field of classical Quantative Structure Activity Relationships (QSAR), as we know it today, began with the seminal work of Hansch and Fujita[1]. QSAR related biological activity for members of a congeneric series with substituted parameters. QSAR represents an important stage in the development of our understanding of the fundamentals of the processes and factors controlling drug action, and has provided man... |
| File Format | |
| Publisher Date | 1996-01-01 |
| Access Restriction | Open |
| Subject Keyword | Evolutionary Neural Network Dihydrofolate Reductase Inhibitor Quantitative Structure Activity Relationship Seminal Work Multifactor Data Genetic Algorithm Accurate Prediction Feedforward Neural Network Hidden Layer Insignificant Predictor Statistical Method Drug Action Substituted Parameter Biological Activity Congeneric Series New System Important Stage Fine Tune Qsar Related Biological Activity Evolutionary Neural Netwoks Classical Quantative Structure Activity Relationship |
| Content Type | Text |
