Review of QSAR Models for Enzyme Classes of Drug Targets: Theoretical Background and Applications in Parasites, Hosts and Other Organisms

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The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. Many of these proteins are relevant for Pharmaceutical Design because they may be enzymes of different classes that could become drug targets. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a review and discussion of Alignment-Free Methods (AFMs) for fast prediction of the Enzyme Classification (EC) number from structural patterns. We referred to both methods based on linear techniques such as Linear Discriminant Analysis (LDA) and/or non-linear models like Artificial Neural Networks (ANN) or Support Vector Machine (SVM) in order to compare linear vs. nonlinear classifiers. We also detected which of these models have been implemented as Web Servers free to the public and compiled a list of some of these websites. For instance, we reviewed the servers implemented at portal Bio-AIMS ( AIMS/EnzClassPred.php) and the server EzyPred (
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  • Current Pharmaceutical Design publishes timely in-depth reviews covering all aspects of current research in rational drug design. Each issue is devoted to a single major therapeutic area. A Guest Editor who is an acknowledged authority in a therapeutic field has solicits for each issue comprehensive and timely reviews from leading researchers in the pharmaceutical industry and academia.

    Each thematic issue of Current Pharmaceutical Design covers all subject areas of major importance to modern drug design, including: medicinal chemistry, pharmacology, drug targets and disease mechanism.
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