Prediction Enhancement of Protein-Water Binding Conservation through Evolutionary Computation

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The design of drugs to combat various diseases is an extremely expensive and time-consuming process. Potentially, computational ligand screening will reduce the time and expense associated with drug lead discovery. Correctly predicting sites of water conservation on a protein surface can significantly increase the accuracy of ligand screening efforts. Traditional classification methods make correct predictions with approximately 60 accuracy. The goal of our research is to improve prediction accuracy by applying evolutionary computing (EC) to traditional methods of data classification. We present a method that improves accuracy by applying EC feature selection and extraction techniques to k-nearest neighbor and naïve Bayes classifiers. In order to facilitate this research, a versatile EC engine was developed in Java. Despite Javas object oriented nature, few general-purpose Java-based EC engines exist. Our engine, with several unique features, will therefore be useful to the EC community, and will be available via the World Wide Web.


Presented at the Symposium on Bioinformatics for Drug Development, Toledo, OH, November 16-17, 2001.