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Information 2010, 1(2), 60-73; doi:10.3390/info1020060

Application of Information—Theoretic Concepts in Chemoinformatics

Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
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Received: 1 September 2010 / Revised: 26 September 2010 / Accepted: 16 October 2010 / Published: 20 October 2010
(This article belongs to the Special Issue What Is Information?)
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Abstract

The use of computational methodologies for chemical database mining and molecular similarity searching or structure-activity relationship analysis has become an integral part of modern chemical and pharmaceutical research. These types of computational studies fall into the chemoinformatics spectrum and usually have large-scale character. Concepts from information theory such as Shannon entropy and Kullback-Leibler divergence have also been adopted for chemoinformatics applications. In this review, we introduce these concepts, describe their adaptations, and discuss exemplary applications of information theory to a variety of relevant problems. These include, among others, chemical feature (or descriptor) selection, database profiling, and compound recall rate predictions.
Keywords: database profiling; feature selection; feature significance; information theory; similarity searching; molecular topology; virtual screening database profiling; feature selection; feature significance; information theory; similarity searching; molecular topology; virtual screening
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Vogt, M.; Wassermann, A.M.; Bajorath, J. Application of Information—Theoretic Concepts in Chemoinformatics. Information 2010, 1, 60-73.

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