have an extraordinarily difficult and ex-pensive task ahead. Can computationaltechniques facilitate and accelerate thedrug discovery process? This is one ofthe key questions addressed by theauthor. The book suggests that theanswer may be positive, provided thatcomputational approaches to drug dis-covery are applied with a very in-depthknowledge of their possibilities and limi-tations.

The book is divided into three majorparts. Part I introduces the reader to thedrug-design process. Chapter 1 describesthe major physicochemical and biologi-cal properties that make a molecule apotential drug. It then sets out the firststeps of the drug-discovery process. Aclear picture emerges of the target-cen-tric era of modern drug discovery. When-ever structural information of the targetprotein is available, structure-based drugdesign is applied to a greater extentthan ligand-based approaches. This sec-tion of the book gives the reader a tasteof the major experimental techniquesused to determine the three-dimensionalstructure of proteins.

Part II details the homology modelingtechnique and the use of computationalapproaches to protein folding. It repre-sents the core of the book, where theauthor describes and critically discussesmost computational methods used indrug design. The chapter on dockingsimulations is fairly comprehensive, asare the chapters dealing with QSAR and3D-QSAR. However, molecular and quan-tum mechanics are only briefly touchedon. The author’s intention to completelyavoid mathematical terms means thatthese chapters are accessible to nonspe-cialists.

Part II also covers de novo approachesto drug design, as well as a chapterdedicated to cheminformatics. The latteris described in detail, further highlight-ing the industry-centric nature of thebook. Cheminformatics is a crucial infor-mation-technology tool for industrialdrug discovery, yet it is seldom utilizedor even taught at an academic level. Thissection also includes a chapter dedicatedto pharmacokinetics and drug toxicity.Calculation of the ADMET profile of newcompounds is a valuable computationaltool owing to a high attrition rate in

drug discovery due to poor pharmacoki-netics and drug toxicity. As reported bythe author, despite an overall limited ac-curacy, ADMET predictions are widely ex-ploited to prevent even the remote pos-sibility that a novel drug candidate willfail later in the costly clinical phases.

Part III contains some “Related Topics”dealing with bioinformatics, simulationsof complex systems at the cellular andorgan level, synthesis route predictionand prodrugs. The final chapter de-scribes future developments in drugdesign. There are some broader scientifictopics that will probably be exploited toenhance the drug discovery process.This chapter reports some relevant ex-amples, ranging from individual patientgenome sequencing to proteomics, stemcells, etc.

This is a very useful book for those en-tering the field of computational drugdesign. It is suitable for a nonspecialistreadership due to the large amount oftopics covered (albeit some more con-cisely than others) and its lack of mathe-matical terms.

In conclusion, this book provides acomprehensive introduction to computa-tional drug design for scientists (e.g. me-dicinal chemists and pharmacologists,particularly at industrial level) who arenot familiar with computational methodsand who wish to discuss simulation out-comes with colleagues from computa-tional departments. It provides a similar-ly comprehensive introduction for stu-dents, while also covering aspects notusually touched on by other computa-tional textbooks. Students will then needto deepen their mathematical and theo-retical background to become robustcomputational drug designers.

Dr. Andrea CavalliUniversit� di Bologna and IstitutoItaliano di Tecnologia (Italy)

[1] W. L. Jorgensen, Science 2004, 303, 1813 –1818.

Molecular Descriptors forChemoinformatics (2nd ed.)By Roberto Todeschini andViviana Consonni.

Wiley-VCH, Weinheim 2009. 1257 pp. (2 vol-umes), hardcover E 379.00.—ISBN 978-3-527-31852-0

Quantitative struc-ture–activity rela-tionship (QSAR) isone of mostwidely used tech-niques of data fit-ting in the largearea that is nowreferred to asChemoinformat-ics, or to be moreprecise as Chemometrics. Before any pre-dictions for new compounds can bemade, a suitable regression equation hasto be generated. That requires experi-mentally obtained data for the set ofmolecules being used for training and areasonable choice of molecular descrip-tors describing those. Once a regressionequation has been obtained that relatesthese descriptors to the measured quan-tity, predictions for new molecules arepossible. Most chemists will agree thatchoosing suitable descriptors is a formof art, involving experience, chemical in-tuition, and serendipity. This is, however,only in part due to the vast number ofpublished descriptors.

It is difficult to find a collection of de-scriptors that is equally comprehensiveas this two-volume book for several rea-sons: Firstly, because molecular descrip-tors are similarly manifold as chemicalspace itself is, it renders the processingof all relevant publications of newly re-ported, as well as previously established,descriptors and applications an endlesstask. For example, the number of citedreferences has almost doubled since thefirst edition, which dates back to theyear 2000. Therefore, computer pro-grams typically contain only a selection,respectively subsets of all reported de-scriptors.

Secondly, the task of compiling a com-prehensive collection is fairly unattrac-tive if you are not involved in writingcomputer programs that calculate such

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descriptors. In turn, it is obvious to col-lect descriptors systematically for doingso. As for Todeschini and Consonni, bothmotives match. They are among the au-thors of the well-known computer pro-gram Dragon, which incorporates thistremendous number of descriptors, andlikewise it is not surprising to find theirnames on the cover of this book. More-over, Todeschini initiated the web sitehttp://www.moleculardescriptors.eu topromote information exchange. One istherefore temped to assume that suchprerequisites would narrow the reader-ship to experts in the field, because thefocus is clearly set on a comprehensivecollection of descriptors, which occupiesthe largest part of the first volume. Thechapter on QSAR modelling is converselyrather concise and emerging topics, suchas interpretable descriptors and chancecorrelation, are just briefly mentioned inthe introduction. The subsequent chap-ter summarizes some of the less knownhistorical milestones in the developmentof QSAR and deserves special attention.Therefore, this compendium maybecome important to a nonexpert overtime and with increasing necessity tobuild new QSAR models, just as the titlespecifies this two-volume issue as a ref-erence book.

There are several possible ways of or-ganizing descriptors, whereby the obvi-ous approach consists of sorting themaccording to category or complexity andattaching an alphabetic index that liststhe actual page where the descriptorcan be found. Corresponding categoriesare, for example, 1D-, 2D- and 3D-de-scriptors that are derived from the ac-cording representation of the molecule,namely constitution, topology and three-dimensional structure. These can be fur-ther subdivided into groups accordingto other criteria. Todeschini and Conson-ni, however, chose a different way: de-scriptors are treated in alphabeticalorder and are mentioned as keywordswith cross-references to categories.These comprise numerical quantitiessuch as steric, topological, substructure,electronic, matrix-derived, quantumchemical, physicochemical, spectral, simi-larity, vectorial and lipophilicity descrip-tors. Each of these descriptor categories,as well as important techniques such asCoMFA, is introduced briefly with cross-references to those descriptors that areused for the respective method.

Besides the comprehensive listing ofdescriptors this is the actual strength ofthe book. Even experts in the field willfind these sections helpful, because

these are at the level of reviews. Formany descriptors, examples are providedthat simplify understanding and transferof the descriptors to own computer pro-grams. In practice, a separate alphabeti-cal index would have facilitated finding aspecific descriptor. For example, lookingup “MACCS keys”, the reader is referredto the section “substructure descriptors”that also contains further similar finger-print descriptors. Fortunately, the actualdescriptor names are printed in bold,which limits the unavoidable browsingthrough the pages. Corresponding refer-ences to the original publications arecited using author names and year,whereas the full citations can be foundseparately in the second volume, wherethe author names are alphabeticallysorted. Despite these minor shortcom-ings, the present two volumes are a wel-come aid for those involved in develop-ing QSAR models or computer programsthat require molecular descriptors.

Dr. Michael C. HutterSaarland University (Germany)DOI: 10.1002/cmdc.200900399

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