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Chemoinformatics de Jürgen Bajorath

Chemoinformatics

Editat de Jürgen Bajorath
en Limba Engleză Hardback – 12 mai 2004

Merită menționat că volumul Chemoinformatics este susținut de o bibliografie extensivă de rapoarte de cercetare originale și resurse metodologice, oferind acces la strategii testate în laboratoarele de cercetare farmaceutică internațională. Putem afirma că lucrarea coordonată de Jürgen Bajorath nu se limitează la teorie, ci funcționează ca un ghid tehnic riguros, axat pe ceea ce funcționează și ce eșuează în practica curentă a descoperirii medicamentelor.

Ca și Barry A. Bunin în Chemoinformatics: Theory, Practice, & Products, autorul distilează experiență reală în principii acționabile, însă Jürgen Bajorath pune un accent deosebit pe integrarea chimiei cu informatica și biologia chimică. Această abordare este o continuare firească a temelor explorate de autor în lucrarea sa anterioară, Chemoinformatics and Computational Chemical Biology, unde analiza interacțiunilor țintă-ligand și designul bibliotecilor de compuși erau piloni centrali. În volumul de față, perspectiva este extinsă către infrastructura sistemelor și optimizarea lead-urilor în context industrial.

Structura cărții este organizată logic, în 15 capitole, facilitând o progresie tehnică de la bazele modelării la aplicații complexe. Primele secțiuni funcționează ca un tutorial critic pentru evitarea capcanelor în construirea modelelor predictive, urmate de analize avansate precum teoria mulțimilor brute pentru expresia genelor și modelele Naïve Bayesian. Această organizare permite cercetătorilor să navigheze de la evaluarea screening-ului virtual până la implementarea unor sisteme integrate, beneficiind de expertiza cumulată a peste trei decenii de dezvoltare software în domeniu.

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Specificații

ISBN-13: 9781588292612
ISBN-10: 1588292614
Pagini: 540
Ilustrații: XIV, 524 p.
Dimensiuni: 160 x 241 x 38 mm
Greutate: 1.07 kg
Ediția:2004
Editura: Humana
Locul publicării:Totowa, NJ, United States

Public țintă

Research

De ce să citești această carte

Această lucrare este esențială pentru cercetătorii din chimia computațională și farmaceutică, oferind un set de bune practici validate industrial. Cititorul câștigă acces la studii de caz reale și metodologii de optimizare care reduc riscul de eroare în modelarea predictivă. Este un instrument practic pentru oricine dorește să înțeleagă cum infrastructura informatică poate accelera descoperirea de noi compuși terapeutici, economisind timp și resurse experimentale.

Despre autor

Jürgen Bajorath este un expert recunoscut la nivel mondial în domeniul chimioinformaticii și al modelării computaționale aplicate în biologia chimică. Cu o carieră marcată de numeroase contribuții în designul medicamentelor și analiza structură-activitate, el ocupă poziții academice de prestigiu și colaborează strâns cu industria farmaceutică. Lucrările sale sunt fundamentale pentru înțelegerea modului în care datele chimice complexe pot fi transformate în cunoștințe aplicabile pentru identificarea de noi ținte terapeutice.

Descriere scurtă

In the literature, several terms are used synonymously to name the topic of this book: chem-, chemi-, or chemo-informatics. A widely recognized de- nition of this discipline is the one by Frank Brown from 1998 (1) who defined chemoinformatics as the combination of “all the information resources that a scientist needs to optimize the properties of a ligand to become a drug. ” In Brown’s definition, two aspects play a fundamentally important role: de- sion support by computational means and drug discovery, which distinguishes it from the term “chemical informatics” that was introduced at least ten years earlier and described as the application of information technology to ch- istry (not with a specific focus on drug discovery). In addition, there is of course “chemometrics,” which is generally understood as the application of statistical methods to chemical data and the derivation of relevant statistical models and descriptors (2). The pharmaceutical focus of many developments and efforts in this area—and the current popularity of gene-to-drug or si- lar paradigms—is further reflected by the recent introduction of such terms as “discovery informatics” (3), which takes into account that gaining kno- edge from chemical data alone is not sufficient to be ultimately successful in drug discovery. Such insights are well in accord with other views that the boundaries between bio- and chemoinformatics are fluid and that these d- ciplines should be closely combined or merged to significantly impact b- technology or pharmaceutical research (4).

Cuprins

Molecular Similarity Measures.- Evaluation of Molecular Similarity and Molecular Diversity Methods Using Biological Activity Data.- A Web-Based Chemoinformatics System for Drug Discovery.- Application of Chemoinformatics to High-Throughput Screening.- Strategies for the Identification and Generation of Informative Compound Sets.- Methods for Applying the Quantitative Structure-Activity Relationship Paradigm.- 3D-LogP.- Derivation and Applications of Molecular Descriptors Based on Approximate Surface Area.- Cell-Based Partitioning.- Partitioning in Binary-Transformed Chemical Descriptor Spaces.- Comparison of Methods Based on Diversity and Similarity for Molecule Selection and the Analysis of Drug Discovery Data.- Using Recursive Partitioning Analysis to Evaluate Compound Selection Methods.- Designing Combinatorial Libraries Optimized on Multiple Objectives.- Approaches to Target Class Combinatorial Library Design.- Simulated Annealing.- Genetic Algorithms for Classification of Olfactory Stimulants.- How to Describe Chirality and Conformational Flexibility.- Novel Scoring Methods in Virtual Ligand Screening.- Prediction of Drug-Like Molecular Properties.

Recenzii

"...a 'how-to' guide to chemoinformatics and how to use it in drug discovery...excellent reference material and is a worthwhile addition to the library of most computational chemists and to medical chemists." - Journal of Medicinal Chemistry

Textul de pe ultima copertă

In the post-genomic era, the role informatics technologies plays in chemical and pharmaceutical research has become increasingly important in data management and lead-compound identification. In Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery, well-recognized pioneers and investigators from diverse professional environments survey the key concepts in the field, describe cutting-edge methods, and provide exemplary pharmaceutical applications. The authors explain the theory behind the crucial concepts of molecular similarity and diversity, describe the challenging efforts to use chemoinformatics approaches to virtual and high-throughput screening, and illuminate the latest developments in multidimensional QSAR analysis. Other topics of interest include the use of partitioning algorithms and classification methods for analyzing large compound databases, screening sets, and virtual screening for active molecules; different approaches to target class-specific library design; the generation of novel classes of molecular descriptors; and Web-based tools for chemical database access and management. Also presented are different methods for describing molecular chirality and conformational parameters and for predicting the drug-like character and basic ADME properties of compounds based on modeling their putative interactions with cytochrome P450 isoforms.
State-of-the-art and user-friendly, Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery illuminates the conceptual and methodological diversity of this rapidly evolving field and offers instructive examples of cutting-edge applications in the drug discovery process.

Notă biografică

JÜRGEN BAJORATH, PhD, is Chair of Life Science Informatics at the University of Bonn and also an Affiliate Professor in the Department of Biological Structure at the University of Washington. In addition, he has more than 10 years' experience in drug disovery. His research focuses on the development of computational methods for medicinal chemistry and chemical biology. Dr. Bajorath holds 26 patents, has authored more than 400 scientific articles, and edited four books.

Descriere

Descriere de la o altă ediție sau format:

Chemoinformatics strategies to improve drug discovery results With contributions from leading researchers in academia and the pharmaceutical industry as well as experts from the software industry, this book explains how chemoinformatics enhances drug discovery and pharmaceutical research efforts, describing what works and what doesn't. Strong emphasis is put on tested and proven practical applications, with plenty of case studies detailing the development and implementation of chemoinformatics methods to support successful drug discovery efforts. Many of these case studies depict groundbreaking collaborations between academia and the pharmaceutical industry. Chemoinformatics for Drug Discovery is logically organized, offering readers a solid base in methods and models and advancing to drug discovery applications and the design of chemoinformatics infrastructures. The book features 15 chapters, including: * What are our models really telling us? A practical tutorial on avoiding common mistakes when building predictive models * Exploration of structure-activity relationships and transfer of key elements in lead optimization * Collaborations between academia and pharma * Applications of chemoinformatics in pharmaceutical research-experiences at large international pharmaceutical companies * Lessons learned from 30 years of developing successful integrated chemoinformatic systems Throughout the book, the authors present chemoinformatics strategies and methods that have been proven to work in pharmaceutical research, offering insights culled from their own investigations. Each chapter is extensively referenced with citations to original research reports and reviews. Integrating chemistry, computer science, and drug discovery, Chemoinformatics for Drug Discovery encapsulates the field as it stands today and opens the door to further advances.