Application of parameter optimization to molecular comparison problems.

Various bioinformatics comparison problems require optimizing several different properties simultaneously. Often linear objective functions combine the values for different properties of solution candidates into a single score to allow for multivariate optimization. In this context, an essential question is how each property should be weighted. Frequently, no apparent measure is available to serve as a model for the score. However, if preferences of certain solution candidates over others in a training set are available, the implied partial ordering may be used to best possibly adjust the weights. We apply different strategies to optimize the parameterization of empirical scoring functions used for two molecular comparison problems, protein threading and small molecule superposition. Using well established evaluation methods, it can be shown that the results of both comparison methods are significantly improved by systematically choosing appropriate weights for the scoring function contributions.