Automated regression algorithms

FlexX Automated docking program with incremental construction algorithm and regression-based scoring function... 

SLIDE Automated docking program with multilevel and mean field algorithm and regression-based scoring function 189 www.bch.msu.edu/labs/kuhn/web/projects/slide/ home.html... 

An automated descriptor selection routine for neural networks using GSA has been shown to be successful. Neural network models found using this algorithm are similar or superior to neural network models found using regression techniques, which might be due to... 

Numerous software data treatments authorize the elucidation of mixture composition from spectra. One of the best-known methods is the Kalman s least squares filter algorithm, which operates through successive approximations based upon calculations using weighted coefficients (additivity law of absorbances) of the individual spectra of each components contained in the spectral library. Other software for determining the concentration of two or more components within a mixture uses vector quantification mathematics. These are automated methods better known by their initials PLS (partial least square), PCR (principal component regression), or MLS (multiple least squares) (Figure 9.26). 

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. 

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