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Home > Computers > Computer Graphics > Reviews in Computational Chemistry, Volume 20-eBook
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THIS VOLUME, LIKE THOSE PRIOR TO IT, FEATURES CHAPTERS BY EXPERTS IN VARIOUS FIELDS OF COMPUTATIONAL CHEMISTRY. TOPICS COVERED IN VOLUME 20 INCLUDE VALENCE THEORY, ITS HISTORY, FUNDAMENTALS, AND APPLICATIONS; MODELING OF SPIN-FORBIDDEN REACTIONS; CALCULATION OF THE ELECTRONIC SPECTRA OF LARGE MOLECULES; SIMULATING CHEMICAL WAVES AND PATTERNS; FUZZY SOFT-COMPUTING METHODS AND THEIR APPLICATIONS IN CHEMISTRY; AND DEVELOPMENT OF COMPUTATIONAL MODELS FOR ENZYMES, TRANSPORTERS, CHANNELS, AND RECEPTORS RELEVANT TO ADME/TOX.
FROM REVIEWS OF THE SERIES
"Reviews in Computational Chemistry remains the most valuable reference to methods and techniques in computational chemistry."
-JOURNAL OF MOLECULAR GRAPHICS AND MODELING
"One cannot generally do better than to try to find an appropriate article in the highly successful Reviews in Computational Chemistry. The basic philosophy of the editors seems to be to help the authors produce chapters that are complete, accurate, clear, and accessible to experimentalists (in particular) and other nonspecialists (in general)."
-JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
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Title of ebook: Reviews in Computational Chemistry, Volume 20
ISBN: 9780471678847
Publisher: Wiley-VCH
Internet download file size: 3332 kb
Released online for download: 08-02-2004
Editor: Lipkowitz, Kenny, B.
Editor: Larter, Raima
Editor: Cundari, Tom


Reviews in Computational Chemistry


Chapter One

Clustering Methods and Their Uses in Computational Chemistry

Geoff M. Downs and John M. Barnard

Barnard Chemical Information Ltd., 46 Uppergate Road, Stannington, Sheffield S6 6BX, United Kingdom

INTRODUCTION

Clustering is a data analysis technique that, when applied to a set of heterogeneous items, identifies homogeneous subgroups as defined by a given model or measure of similarity. Of the many uses of clustering, a prime motivation for the increasing interest in clustering methods is their use in the selection and design of combinatorial libraries of chemical structures pertinent to pharmaceutical discovery.

One feature of clustering is that the process is unsupervised, that is, there is no predefined grouping that the clustering seeks to reproduce. In contrast to supervised learning, where the task is to establish r ... read full excerpt from Reviews in Computational Chemistry, Volume 20 ebook





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