Author: Klaus Gundertofte
Publisher: Springer Science & Business Media
ISBN: 1461541417
Category : Science
Languages : en
Pages : 490
Book Description
Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1.
Molecular Modeling and Prediction of Bioactivity
Author: Klaus Gundertofte
Publisher: Springer Science & Business Media
ISBN: 1461541417
Category : Science
Languages : en
Pages : 490
Book Description
Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1.
Publisher: Springer Science & Business Media
ISBN: 1461541417
Category : Science
Languages : en
Pages : 490
Book Description
Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1.
Molecular Modeling and Prediction of Bioactivity
Author: Klaus Gundertofte
Publisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 528
Book Description
The book covers highly important topics in the challenging process from lead finding to drug candidates. Focus is upon the potential usefulness of methods for design of lead discovery libraries, lead optimisation, computational chemistry methods for the calculation of energetics of protein-ligand interaction, and computer simulations of biological activities. Important topics include new developments in chemometrics and rational molecular design as well as different aspects of structure representation, knowledge-based approaches to structure identification, and information handling.
Publisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 528
Book Description
The book covers highly important topics in the challenging process from lead finding to drug candidates. Focus is upon the potential usefulness of methods for design of lead discovery libraries, lead optimisation, computational chemistry methods for the calculation of energetics of protein-ligand interaction, and computer simulations of biological activities. Important topics include new developments in chemometrics and rational molecular design as well as different aspects of structure representation, knowledge-based approaches to structure identification, and information handling.
Molecular Theory of Solvation
Author: F. Hirata
Publisher: Springer Science & Business Media
ISBN: 1402025904
Category : Science
Languages : en
Pages : 366
Book Description
Molecular Theory of Solvation presents the recent progress in the statistical mechanics of molecular liquids applied to the most intriguing problems in chemistry today, including chemical reactions, conformational stability of biomolecules, ion hydration, and electrode-solution interface. The continuum model of "solvation" has played a dominant role in describing chemical processes in solution during the last century. This book discards and replaces it completely with molecular theory taking proper account of chemical specificity of solvent. The main machinery employed here is the reference-interaction-site-model (RISM) theory, which is combined with other tools in theoretical chemistry and physics: the ab initio and density functional theories in quantum chemistry, the generalized Langevin theory, and the molecular simulation techniques. This book will be of benefit to graduate students and industrial scientists who are struggling to find a better way of accounting and/or predicting "solvation" properties.
Publisher: Springer Science & Business Media
ISBN: 1402025904
Category : Science
Languages : en
Pages : 366
Book Description
Molecular Theory of Solvation presents the recent progress in the statistical mechanics of molecular liquids applied to the most intriguing problems in chemistry today, including chemical reactions, conformational stability of biomolecules, ion hydration, and electrode-solution interface. The continuum model of "solvation" has played a dominant role in describing chemical processes in solution during the last century. This book discards and replaces it completely with molecular theory taking proper account of chemical specificity of solvent. The main machinery employed here is the reference-interaction-site-model (RISM) theory, which is combined with other tools in theoretical chemistry and physics: the ab initio and density functional theories in quantum chemistry, the generalized Langevin theory, and the molecular simulation techniques. This book will be of benefit to graduate students and industrial scientists who are struggling to find a better way of accounting and/or predicting "solvation" properties.
Molecular Structure Description
Author: Lemont Burwell Kier
Publisher:
ISBN: 9780124065550
Category : Medical
Languages : en
Pages : 286
Book Description
The electrotopological state is a new approach to defining key structural features of a molecule in drug design. Combining both electronic and topological attributes, the E-State index facilitates the development of a structure - activity model, the definition of a pharmacophore, and the search through a database for similar or dissimilar compounds. The background for the method, the concept of the intrinsic state, and the E-State index as a function of the atom or group within the field of all atoms in a molecule are all described in this primer for a new structure paradigm. Software on the bundled CD-ROM allows the reader to compute and display the E-State indices for molecules, while examples in the book illustrate strategies that can be used in drug research.
Publisher:
ISBN: 9780124065550
Category : Medical
Languages : en
Pages : 286
Book Description
The electrotopological state is a new approach to defining key structural features of a molecule in drug design. Combining both electronic and topological attributes, the E-State index facilitates the development of a structure - activity model, the definition of a pharmacophore, and the search through a database for similar or dissimilar compounds. The background for the method, the concept of the intrinsic state, and the E-State index as a function of the atom or group within the field of all atoms in a molecule are all described in this primer for a new structure paradigm. Software on the bundled CD-ROM allows the reader to compute and display the E-State indices for molecules, while examples in the book illustrate strategies that can be used in drug research.
QSAR
Author: Hugo Kubinyi
Publisher: John Wiley & Sons
ISBN: 3527616837
Category : Science
Languages : de
Pages : 252
Book Description
Finding the new remedy for a certain disease: an inspired goal. QSAR, an invaluable tool in drug design, aids scientists to attain this aim. This book is a long-awaited comprehensive text to QSAR and related approaches. It provides a practice-oriented introduction to the theory, methods and analyses for QSAR relationships, including modelling-based and 3D approaches. Hugo Kubinyi is a leading expert in QSAR. Readers will benefit from the author's 20 years of practical experience, from his careful calculations and recalculations of thousands of QSAR equations. Among the topics covered are: - physiocochemical parameters - quantitative models - statistical methods - Hansch analysis - Free Wilson analysis - 3D-QSAR approaches The book can readily be used as a textbook due to its high didactic value and numerous examples (over 200 equations and 1100 references).
Publisher: John Wiley & Sons
ISBN: 3527616837
Category : Science
Languages : de
Pages : 252
Book Description
Finding the new remedy for a certain disease: an inspired goal. QSAR, an invaluable tool in drug design, aids scientists to attain this aim. This book is a long-awaited comprehensive text to QSAR and related approaches. It provides a practice-oriented introduction to the theory, methods and analyses for QSAR relationships, including modelling-based and 3D approaches. Hugo Kubinyi is a leading expert in QSAR. Readers will benefit from the author's 20 years of practical experience, from his careful calculations and recalculations of thousands of QSAR equations. Among the topics covered are: - physiocochemical parameters - quantitative models - statistical methods - Hansch analysis - Free Wilson analysis - 3D-QSAR approaches The book can readily be used as a textbook due to its high didactic value and numerous examples (over 200 equations and 1100 references).
'In Silico' Simulation of Biological Processes
Author: Gregory R. Bock
Publisher: John Wiley & Sons
ISBN: 0470857900
Category : Science
Languages : en
Pages : 270
Book Description
Over recent decades vast amounts of biological data have been accumulated. However, it is becoming increasingly difficult to apply traditional theoretical methods to the formulation of coherent pictures of cell and organ function because it is no longer possible for a human theorist to integrate all of the available information. Instead, computer technologies must now be used to perform this integration. This book brings together contributions from many different fields to summarize the current status of computer-assisted modelling of biological processes. The initial chapters deal with fundamental developments in hardware, software and mathematics that underlie current approaches to biological modelling. Next, different approaches to collating data on gene structure and function are presented. These databases form a vital resource for any investigator trying to construct an integrated picture of particular biological systems. Cell signalling systems form a particularly complicated aspect of all cellular function and are important both in the understanding of basic cellular processes and in selecting targets for drugs. Recent approaches to integrating data on cell signalling into computer models are covered. Further chapters build on these approaches to show how computerized models of intact cells can be developed. Finally, approaches to the computer modelling of whole organs such as the heart are presented. The role of computer modelling in drug design is the subject of the final chapter and is also touched on throughout the discussions.
Publisher: John Wiley & Sons
ISBN: 0470857900
Category : Science
Languages : en
Pages : 270
Book Description
Over recent decades vast amounts of biological data have been accumulated. However, it is becoming increasingly difficult to apply traditional theoretical methods to the formulation of coherent pictures of cell and organ function because it is no longer possible for a human theorist to integrate all of the available information. Instead, computer technologies must now be used to perform this integration. This book brings together contributions from many different fields to summarize the current status of computer-assisted modelling of biological processes. The initial chapters deal with fundamental developments in hardware, software and mathematics that underlie current approaches to biological modelling. Next, different approaches to collating data on gene structure and function are presented. These databases form a vital resource for any investigator trying to construct an integrated picture of particular biological systems. Cell signalling systems form a particularly complicated aspect of all cellular function and are important both in the understanding of basic cellular processes and in selecting targets for drugs. Recent approaches to integrating data on cell signalling into computer models are covered. Further chapters build on these approaches to show how computerized models of intact cells can be developed. Finally, approaches to the computer modelling of whole organs such as the heart are presented. The role of computer modelling in drug design is the subject of the final chapter and is also touched on throughout the discussions.
Graph Representation Learning
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Artificial Intelligence in Drug Discovery
Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425
Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425
Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Author:
Publisher: IOS Press
ISBN:
Category :
Languages : en
Pages : 4576
Book Description
Publisher: IOS Press
ISBN:
Category :
Languages : en
Pages : 4576
Book Description
Structure-Based Drug Design
Author: Pandi Veerapandian
Publisher: Routledge
ISBN: 1351413066
Category : Medical
Languages : en
Pages : 665
Book Description
Introducing the most recent advances in crystallography, nuclear magnetic resonance, molecular modeling techniques, and computational combinatorial chemistry, this unique, interdisciplinary reference explains the application of three-dimensional structural information in the design of pharmaceutical drugs. Furnishing authoritative analyses by world-renowned experts, Structure-Based Drug Design discusses protein structure-based design in optimizing HIV protease inhibitors and details the biochemical, genetic, and clinical data on HIV-1 reverse transcriptase presents recent results on the high-resolution three-dimensional structure of the catalytic core domain of HIV-1 integrase as a foundation for divergent combination therapy focuses on structure-based design strategies for uncovering receptor antagonists to treat inflammatory diseases demonstrates a systematic approach to the design of inhibitory compounds in cancer treatment reviews current knowledge on the Interleukin-1 (IL-1) system and progress in the development of IL-1 modulators describes the influence of structure-based methods in designing capsid-binding inhibitors for relief of the common cold and much more!
Publisher: Routledge
ISBN: 1351413066
Category : Medical
Languages : en
Pages : 665
Book Description
Introducing the most recent advances in crystallography, nuclear magnetic resonance, molecular modeling techniques, and computational combinatorial chemistry, this unique, interdisciplinary reference explains the application of three-dimensional structural information in the design of pharmaceutical drugs. Furnishing authoritative analyses by world-renowned experts, Structure-Based Drug Design discusses protein structure-based design in optimizing HIV protease inhibitors and details the biochemical, genetic, and clinical data on HIV-1 reverse transcriptase presents recent results on the high-resolution three-dimensional structure of the catalytic core domain of HIV-1 integrase as a foundation for divergent combination therapy focuses on structure-based design strategies for uncovering receptor antagonists to treat inflammatory diseases demonstrates a systematic approach to the design of inhibitory compounds in cancer treatment reviews current knowledge on the Interleukin-1 (IL-1) system and progress in the development of IL-1 modulators describes the influence of structure-based methods in designing capsid-binding inhibitors for relief of the common cold and much more!