Chemometrics and chemoinformatics
Author(s)
Bibliographic Information
Chemometrics and chemoinformatics
（ACS symposium series, 894）
American Chemical Society, c2005
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Note
"... was held at the 224th American Chemical Society (ACS) National Meeting in Boston, Massachusetts on August 21-22, 2004 ..."--Pref.
Includes bibliographical references and indexes
"Distributed by Oxford University Press"--T.p. verso
Description and Table of Contents
Description
Chemometrics and Chemoinformatics gives chemists and other scientists an introduction to the field of chemometrics and chemoinformatics. Chemometrics is an approach to analytical chemistry based on the idea of indirect observation. Measurements related to the chemical composition of a substance are taken, and the value of a property of interest is inferred from them through some mathematical relation. Basically, chemometrics is a process. Measurements are made, data
is collected, and information is obtained to periodically assess and acquire knowledge. This, in turn, has led to a new approach for solving scientific problems: (1) measure a phenomenon or process using chemical instrumentation that generates data inexpensively, (2) analyze the multivariate data,
(3) iterate if necessary, (4) create and test the model, and (5) develop fundamental multivariate understanding of the process. Chemoinformatics is a subfield of chemometrics, which encompasses the analysis, visualization, and use of chemical structural information as a surrogate variable for other data or information. The boundaries of chemoinformatics have not yet been defined. Only recently has this term been coined. Chemoinformatics takes advantage of techniques from many disciplines such
as molecular modeling, chemical information, and computational chemistry. The reason for the interest in chemoinformatics is the development of experimental techniques such as combinatorial chemistry and high-throughput screening, which require a chemist to analyze unprecedented volumes of data.
Access to appropriate algorithms is crucial if such experimental techniques are to be effectively exploited for discovery. Many chemists want to use chemoinformatic methods in their work but lack the knowledge required to decide which techniques are the most appropriate.
Table of Contents
Preface
1.: Barry K. Lavine and Jerome Workman, Jr.: Chemometrics: Past, Present, and Future
2.: Steven D. Brown, HuWei Tan, and Robert Feudale: Improving the Robustness of Multivariate Calibrations
3.: Fredrik Pettersson and Anders Berglund: Interpretation and Validation of PLS Models for Microarray Data
4.: Ling Xue, Florence L. Stahura, and Jurgen Bajorath: Chemoinformatics: Perspectives and Challenges
5.: G. W. A. Milne: Mathematics as a Basis for Chemistry
6.: John D. Holliday, Naomie Salim, and Peter Willett: On the Magnitudes of Coefficient Values in the Calculation of Chemical Similarity and Dissimilarity
7.: Rajni Garg: Chemoinformatics and Comparative Quantitative Structure-Activity Relationship
8.: Curt M. Breneman, Minghu Song, Jinbo Bi, N. Sukumar, Kristin P. Bennett, Steven Cramer, and N. Tugcu: Prediction of Protein Retention Times in Anion-Exchange Chromatography Systems Using Support Vector Regression
9.: Barry K. Lavine, Charles E. Davidson, Curt Breneman, and William Katt: Analysis of Odor Structure Relationships Using Electonic Van Der Waals Surface Property Descriptors and Genetic Algorithms
10.: Douglas R. Henry and Joseph L. Durant, Jr.: Optimization of MDL Substructure Search Keys for the Prediction of Activity and Toxicity
11.: Norah E. MacCuish and John D. MacCuish: Clustering Compound Data: Asymetric Clustering of Chemical Datasets
12.: Weida Tong, Huixiao Hong, Hong Fang, Qian Xie, Roger Perkins, and John D. Walker: From Decision Tree to Heterogeneous Decision Forest: A Novel Chemometrics Approach for Structure-Activity Relationship Modeling
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