626 J. Chem. Inf. Comput. Sci., Vol. 37, No. 3, 1997 Large scale scientific data projects, both national and international in scope, are used to demonstrate the variety of information issues. Examples of the topics dealt with are as follows: the Internet and Web services, free or fair circulation of scientific data, distributed networks, security, and national legislation. Part 2 provides examples from all over the globe of large information systems in use by the different scientific communities. The essays are written by information scientists from the field and/or the specific data project. Wherever relevant, the Web addresses are provided to the project and all the project participants. The inclusion of these Web addresses is a very helpful aid for the readers, and all of the addresses were correct at the time of review. The monograph’s emphasis is largely on the current state of the technology with some discussion of the direction these different projects may take. It is important to note that the projects in place are by no means of equal strength. Different regions are still dealing with limited internet connection, while others have already provided universal desktop access. East Asia struggles to standardize software languages, hardware, alphabets, and Internet access, while the West pushes toward something ominously called “the federation”. The interesting aspect of the federation is that it aims towards “interoperability” at a local, collaborative, and community level. It is, in essence, the information science manifestation of how scientific progress is attained. It is that unique balance of workstation raw data, finessed into the larger data pool, which is being added to the community’s (biology, chemistry, etc.) greater knowledge. The monograph is exciting in many ways: some organized projects are in the last stages of fine-tuning, while others are just approaching a prototype. However, the issues all parties and countries face, regardless of the strides already made, are issues of standardization, price, fairness of access, flexibility for local environments, and strength, or robustness, for international data collaborations. One chapter discusses the fairness of scientists having to pay for data to which they contribute gratis. This section rings especially true for those working in academic libraries and laboratories; science faculty are only too familiar with the ever-rising prices of scientific journals and databases. The monograph does not assume the reader has extensive information or technological literacy. The early chapters provide elegant and comprehensive overviews of the Internet, Web, hypertext, multimedia, database retrieval, and management. In addition to presenting the issues, the authors also present their solutions to data management and retrieval issues. The problem with this monograph is the same problem that any state of the art analysis has; there are already advances and refinements to much of the software mentioned in the text. This is an insurmountable problem where adVances and state of the art are concerned; this kind of material may be better suited to theme issues of relevant journals. The price of this work, $115.00, is expensive enough that any potential buyer may want to review the book prior to acquisition in order to determine which chapters still apply and which ones are no longer quite so valid. The audience for this work is broad: science and technology libraries, scientific laboratories struggling with work station implementation, students of information science, data management teams, in short any individual involved in the provision, development, and management of scientific data.
Veronica Calderhead Rutgers UniVersity CI9703866 S0095-2338(97)00386-7
Reviews in Computational Chemistry. Vol. 8. Edited by Kenny B. Lipkowitz and Donald B. Boyd. VCH Publishers, Inc., 220 East 23rd Street, New York, N.Y. 10010. xxi + 324 pp., June 1996. List Price $110.00. ISBN 1-56081-929-4 (Hard Copy), ISSN 1069-3599. This series brings together respected experts in the field of computeraided molecular research. Computational chemistry is increasingly used in conjunction with organic, inorganic, medicinal, biological, physical,
BOOK REVIEWS and analytical chemistry. This volume examines various aspects of computations in treating fullerenes and carbon aggregates, pseudopotential calculations of transition metal compounds, core potential approaches to the chemistry of the heavier elements, relativistic effects in chemistry, and the ab initio computation of NMR chemical shielding. This volume, the eighth, of Reviews in Computational Chemistry, represents the editors’ ongoing effort to provide tutorials and reviews for both the novice and the experienced computational chemists. The five chapters are written for newcomers learning about molecular modeling techniques as well as for seasoned professionals who need to acquire expertise in areas outside their own. All the chapters in the volume have a quantum mechanical theme. In Chapter 1, the authors show how ubiquitous semiempirical molecular orbital techniques need to be adjusted to correctly determine the three-dimensional geometries, energies, and properties of fullerenes and carbon aggregates. Chapters 2 and 3 elucidate the so-called effective core potential or pseudopotential methods that have proved invaluble for handling transition metals and other heavy metals. Quantum theory for describing relativistic effects, particularly important to heavy metals, is presented in Chapter 4. In Chapter 5, the author reviews NMR chemical shifts and explains the methodology with examples of heterocycles, buckminsterfullerenes, proteins, and other large molecules. The volume contains an excellent author and subject index. Information about the Reviews in Computational Chemistry is now available on the World Wide Web (http://www.chem.iupui.edu/∼boyd/ rcc.html).
Venkat K. Raman Chemical Abstracts SerVice CI970387Y S0095-2338(97)00387-9
Genetic Algorithms in Molecular Modeling. Edited by James Devillers. Academic Press: London, 1996. xi + 327 pp. $74.95. ISBN 0-12-213810-4. This is the first book in the new series: Principles of QSAR and Drug Design, edited by J. Devilers. The series is a welcome addition to scattered literature on QSAR and drug design in over a dozen journals, and if judged by this first volume, the introduction of the series is timely. QSAR, the quantitative structure-activity relationship, has grown considerably in the last 20 years, not only by the volume of researches devoted to this discipline but also the diversity of methodologies applied to QSAR. For example, the relatively recent methodologies include the partial least squares method, the cell automata, the neural networks, orthogonalized multiple regression analysis, and genetic algorithms, to which this book is devoted. The book consists of a dozen chapters written by leading researchers in the field, starting with introductory chapters on genetic algorithms in computer-aided molecular design (34 pp by J. Devillers), an overview of genetic methods (32 pp by B. T. Luke), and genetic algorithms in feature selection (20 pp by R. Leardi). The remaining eight chapters are devoted to different applications of the genetic algorithm. D. Rogers (22 pp) illustrates nonlinear modeling with splines and makes a comparison between GFA (genetic function approximation) and PLS (partial least squares). He started with a quote of Ernest Rutherford: “If your experiment needs statistics, you ought to have done a better experiment”, which only reminds us about the bias and misunderstanding of statistics at the turn of this century. It would be nice to know what would be the reply of Stainslaw Ulam (the father of the “Monte Carlo” method) to such criticism, but the quote of E. Rutherford is not quite out of place if suitably modified: “If your experiment needs better statistics, you ought to have used better descriptors”. W. J. Dunn and D. Rogers (22 pp) continue with introducing PLS and combining the advantages of PLS (extraction of latent variables approximately along the axes of greatest variations, optimal correlation) with the model generating ability of genetic algorithms to create modified genetic PLS. A. J. Hopfinger and H. C. Patel consider two
