QSPR/QSAR Approaches for Complex Molecular Systems PDF Download
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Author: Sulev Sild
Publisher:
ISBN:
Category : Molecules
Languages : en
Pages : 146
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Book Description
Author: Sulev Sild
Publisher:
ISBN:
Category : Molecules
Languages : en
Pages : 146
Get Book Here
Book Description
Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 3527645012
Category : Medical
Languages : en
Pages : 437
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Book Description
This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR. The high-profile international author and editor team ensures excellent coverage of the topic, making this a must-have for everyone working in chemoinformatics and structure-oriented drug design.
Author: Kunal Roy
Publisher: Springer
ISBN: 3319172816
Category : Science
Languages : en
Pages : 129
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Book Description
This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and pharmacy. With this in mind, the authors have carefully compiled this brief in order to provide a thorough and painless introduction to the fundamental concepts of QSAR/QSPR modelling. The brief is aimed at novice readers.
Author: Alla P. Toropova
Publisher: Springer Nature
ISBN: 3031284011
Category : Science
Languages : en
Pages : 470
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Book Description
This contributed volume overviews recently presented approaches for carrying out QSPR/QSAR analysis by using a simplifying molecular input-line entry system (SMILES) to represent the molecular structure. In contrast to traditional SMILES, quasi-SMILES is a sequence of special symbols-codes that reflect molecular features and codes of experimental conditions. SMILES and quasi-SMILES serve as a basis to develop QSPR/QSAR as well Nano-QSPR/QSAR via the Monte Carlo calculation that provides the so-called optimal descriptors for QSPR/QSAR models. The book presents a reliable technology for developing Nano-QSPR/QSAR while it also includes the description of the algorithms of the Monte Carlo optimization. It discusses the theory and practice of the technique of variational authodecoders (VAEs) based on SMILES and analyses in detail the index of ideality of correlation (IIC) and the correlation intensity index (CII) which are new criteria for the predictive potential of the model. The mathematical apparatus used is simple so that students of relevant specializations can easily follow. This volume is a valuable contribution to the field and will be of great interest to developers of models of physicochemical properties and biological activity, chemical technologists, and toxicologists involved in the area of drug design.
Author: Mark S. Gordon
Publisher: John Wiley & Sons
ISBN: 1119129249
Category : Science
Languages : en
Pages : 376
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Book Description
Fragmentation: Toward Accurate Calculations on Complex Molecular Systems introduces the reader to the broad array of fragmentation and embedding methods that are currently available or under development to facilitate accurate calculations on large, complex systems such as proteins, polymers, liquids and nanoparticles. These methods work by subdividing a system into subunits, called fragments or subsystems or domains. Calculations are performed on each fragment and then the results are combined to predict properties for the whole system. Topics covered include: Fragmentation methods Embedding methods Explicitly correlated local electron correlation methods Fragment molecular orbital method Methods for treating large molecules This book is aimed at academic researchers who are interested in computational chemistry, computational biology, computational materials science and related fields, as well as graduate students in these fields.
Author: Jean-Louis Rivail
Publisher: Springer
ISBN: 3319216260
Category : Science
Languages : en
Pages : 524
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Book Description
This multi-author contributed volume includes methodological advances and original applications to actual chemical or biochemical phenomena which were not possible before the increased sophistication of modern computers. The chapters contain detailed reviews of the developments of various computational techniques, used to study complex molecular systems such as molecular liquids and solutions (particularly aqueous solutions), liquid-gas, solid-gas interphase and biomacromolecular systems. Quantum modeling of complex molecular systems is a useful resource for graduate students and fledgling researchers and is also an excellent companion for research professionals engaged in computational chemistry, material science, nanotechnology, physics, drug design, and molecular biochemistry.
Author: Rajarshi Guha
Publisher: John Wiley & Sons
ISBN: 0470384417
Category : Science
Languages : en
Pages : 299
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Book Description
A breakthrough guide employing knowledge that unites cheminformatics and bioinformatics as innovation for the future Bridging the gap between cheminformatics and bioinformatics for the first time, Computational Approaches in Cheminformatics and Bioinformatics provides insight on how to blend these two sciences for progressive research benefits. It describes the development and evolution of these fields, how chemical information may be used for biological relations and vice versa, the implications of these new connections, and foreseeable developments in the future. Using algorithms and domains as workflow tools, this revolutionary text drives bioinformaticians to consider chemical structure, and similarly, encourages cheminformaticians to consider large biological systems such as protein targets and networks. Computational Approaches in Cheminformatics and Bioinformatics covers: Data sources available for modelling and prediction purposes Developments of conventional Quantitative Structure-Activity Relationships (QSAR) Computational tools for manipulating chemical and biological data Novel ways of probing the interactions between small molecules and proteins Also including insight from public (NIH), academic, and industrial sources (Novartis, Pfizer), this book offers expert knowledge to aid scientists through industry and academic study. The invaluable applications for drug discovery, cellular and molecular biology, enzymology, and metabolism make Computational Approaches in Cheminformatics and Bioinformatics the essential guidebook for evolving drug discovery research and alleviating the issue of chemical control and manipulation of various systems.
Author: Carlo Giuseppe Bertinetto
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659271090
Category :
Languages : en
Pages : 192
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Book Description
This work describes and discusses an innovative approach for the prediction of physical, chemical and biological properties of compounds, ranging from small molecules to large polymers. It is based on the direct and adaptive treatment of molecular structure by means of a Recursive Neural Network (RNN) to derive Quantitative Structure-Property/Activity Relationships (QSPR/QSARs). Chemical compounds are represented through appropriate graphical tools that bypass the need for numerical descriptors. The capabilities of this methodology are investigated by applying it to different predictive problems: the melting point of ionic liquids, the glass transition temperature of polymers and the toxicity of organic molecules. The results show that the graphical molecular representation was able to effectively model each case, providing accurate predictions using practically no background knowledge. The proposed structure-based RNN approach, it is argued, can provide a simple and general prediction method with great potential in molecular design, toxicology and evaluation of other complex properties.
Author: Lodhi, Huma
Publisher: IGI Global
ISBN: 1615209123
Category : Computers
Languages : en
Pages : 418
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Book Description
"This book is a timely compendium of key elements that are crucial for the study of machine learning in chemoinformatics, giving an overview of current research in machine learning and their applications to chemoinformatics tasks"--Provided by publisher.
Author: Mircea V. Diudea
Publisher: Nova Biomedical Books
ISBN: 9781560728597
Category : Chemical models
Languages : en
Pages : 0
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Book Description
The central problem in QSPR (Quantitative Structure-Property Relationship) I QSAR (Quantitative Structure-Activity Relationship) is to convert chemical structures into molecular descriptors that are relevant to a certain physico-chemical property or biological activity. Although various physico-chemical properties may be subjected to QSPR/QSAR studies, the major application field of these models is drug discovery. Classically, drug discovery mainly relied on chance and massive screening of large libraries of synthesised or natural compounds. By contrast, computer-aided drug design, GADD, is an approach to rational drug design made possible by the recent advances in computational chemistry (in its various fields, such as molecular graphics, quantum chemistry, molecular mechanics, molecular dynamics, library searching, prediction of physicochemical and biological properties) and, of course, in computer science. Many physico-chemical properties can be satisfactorily correlated with the topostructural or topochemical features. Topological indices are among the simplest and efficient descriptors for QSPR/QSAR. In principle, these are mathematical objects, without a precise physical meaning. However, they may express the topological shape or may correlate with the molecular volume or surface area, as is shown in the pages of this book.
