Statistical Modelling Of Molecular Descriptors In Qsar Qspr
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Statistical Modelling of Molecular Descriptors in QSAR/QSPR by Matthias Dehmer,Kurt Varmuza,Danail Bonchev Pdf
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.
Recent Advances in QSAR Studies by Tomasz Puzyn,Jerzy Leszczynski,Mark T. Cronin Pdf
This book presents an interdisciplinary overview on the most recent advances in QSAR studies. The first part consists of a comprehensive review of QSAR methodology. The second part highlights the interdisciplinary aspects and new areas of QSAR modelling.
A Primer on QSAR/QSPR Modeling by Kunal Roy,Supratik Kar,Rudra Narayan Das Pdf
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.
Chemometrics Applications and Research by Andrew G. Mercader,Pablo R. Duchowicz,P. M. Sivakumar Pdf
This important new book provides innovative material, including peer-reviewed chapters and survey articles on new applied research and development, in the scientifically important field of QSAR in medicinal chemistry. QSAR is a growing field because available computing power is continuously increasing, QSAR’s potential is enormous, limited only by the quantity and quality of the available experimental input, which are also continuously improving. The number of possible structures for the design of new organic compounds is difficult to imagine, and QSAR helps to predict their activities even before synthesis. The book provides a wealth of valuable information and: • Presents an overview of recent developments in QSAR methodologies along with a brief history of QSAR • Covers the available web resource tools and in silico techniques used in virtual screening and drug discovery processes, compiling an extensive review of web resources in the following categories: databases related to chemical compounds, drug targets, and ADME/toxicity prediction; molecular modeling and drug designing; virtual screening; pharmacophore generation; molecular descriptor calculation software; software for quantum mechanics; ligand binding affinities (docking); and software related to ADME/toxicity prediction • Reviews the rm2 as a more stringent measure for the assessment of model predictivity compared to traditional validation metrics, being specifically important since validation is a crucial step in any QSAR study • Presents linear model improvement techniques that take into account the conformation flexibility of the modeled molecules • Summarizes the building processes of four different pharmacophore models: common-feature, 3D-QSAR, protein-, and protein-ligand complexes • Shows the role of different conceptual density functional theory based chemical reactivity descriptors, such as hardness, electrophilicity, net electrophilicity, and philicity in the design of different QSAR/QSPR/QSTR models • Reviews the use of chemometrics in PPAR research highlighting its substantial contribution in identifying essential structural characteristics and understanding the mechanism of action • Presents the structures and QSARs of antimicrobial and immunosuppressive cyclopeptides, discussing the balance of antimicrobial and haemolytic activities for designing new antimicrobial cyclic peptides • Shows the relationship between DFT global descriptors and experimental toxicity of a selected group of polychlorinated biphenyls, exploring the efficacy of three DFT descriptors • Reviews the applications of Quantitative Structure-Relative Sweetness Relationships (QSRSR), showing that the last decade was marked by an increase in the number of studies regarding QSAR applications for both understanding the sweetness mechanism and synthesizing novel sweetener compounds for the food additive industry The wide coverage makes this book an excellent reference for those in chemistry, pharmacology, and medicine as well as for research centers, governmental organizations, pharmaceutical companies, and health and environmental control organizations.
Molecular Descriptors in QSAR/QSPR by Mati Karelson Pdf
Molecular descriptors are mathematical values that describe the structure or shape of molecules, helping predict the activity and properties of molecules in complex experiments. This book describes the equations known as QSAR (quantitative structure-activity relationships) and QSPR (quantitative structure-property relationships), showing how they can be used productively in a wide range of industries.
Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment by Kunal Roy,Supratik Kar,Rudra Narayan Das Pdf
Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. Includes numerous practical examples related to QSAR methods and applications Follows the Organization for Economic Co-operation and Development principles for QSAR model development Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools
Graph-Theoretical Matrices in Chemistry by Dusanka Janezic,Ante Milicevic,Sonja Nikolic,Nenad Trinajstic Pdf
Graph-Theoretical Matrices in Chemistry presents a systematic survey of graph-theoretical matrices and highlights their potential uses. This comprehensive volume is an updated, extended version of a former bestseller featuring a series of mathematical chemistry monographs. In this edition, nearly 200 graph-theoretical matrices are included. This second edition is organized like the previous one—after an introduction, graph-theoretical matrices are presented in five chapters: The Adjacency Matrix and Related Matrices, Incidence Matrices, The Distance Matrix and Related Matrices, Special Matrices, and Graphical Matrices. Each of these chapters is followed by a list of references. Among the matrices presented several are novel and some are known only to a few. The properties and potential usefulness of many of the presented graph-theoretical matrices in chemistry have yet to be investigated. Most of the graph-theoretical matrices presented have been used as sources of molecular descriptors usually referred to as topological indices. They are particularly concerned with a special class of graphs that represents chemical structures involving molecules. Due to its multidisciplinary scope, this book will appeal to a broad audience ranging from chemistry and mathematics to pharmacology.
Molecular Quantum Similarity in QSAR and Drug Design by R. Carbo-Dorca,D. Robert,L. Amat,X. Girones,E. Besalu Pdf
The authors introduce the concept of Molecular Quantum Similarity, developed in their laboratory, in a didactic form. The basis of the concept combines quantum theoretical calculations with molecular structure and properties even for large molecules. They give definitions and procedures to compute similarities molecules and provide graphical tools for visualization of sets of molecules as n-dimensional point charts.
A Primer on QSAR/QSPR Modeling by Kunal Roy,Supratik Kar,Rudra Narayan Das Pdf
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.
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES by Alla P. Toropova,Andrey A. Toropov Pdf
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.
Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development by Kunal Roy Pdf
Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases
Materials Science and Engineering by Ke Wu,Bharath Natarajan,Lisa Morkowchuk,Mike Krein,Curt M. Breneman Pdf
The Materials Genome Initiative (MGI) was conceived as a unified effort to capture, curate, and exploit materials structure/property information on a grand scale to enable rapid, cost-effective development of novel materials with predictable properties. While the use of “genomic” methods to facilitate property prediction, virtual design, and discovery of materials is relatively new, the concepts driving the development of materials informatics are based, solidly, on the lessons learned during the development history of cheminformatics and bioinformatics. This chapter describes some of the ways in which cheminformatics and machine learning methods have been adapted for, and utilized in, materials science and engineering applications. Examples of how materials quantitative structure–property relationship (MQSPR) models are created, validated, and utilized are presented.
Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development by Sandeep Kumar,Anand Nayyar,Anand Paul Pdf
Healthcare sector is characterized by difficulty, dynamism and variety. In 21st century, healthcare domain is surrounded by tons of challenges in terms of Disease detection, prevention, high costs, skilled technicians and better infrastructure. In order to handle these challenges, Intelligent Healthcare management technologies are required to play an effective role in improvising patient’s life. Healthcare organizations also need to continuously discover useful and actionable knowledge to gain insight from tons of data for various purposes for saving lives, reducing medical operations errors, enhancing efficiency, reducing costs and making the whole world a healthy world. Applying Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development is essential nowadays. The objective of this book is to highlight various Swarm Intelligence and Evolutionary Algorithms techniques for various medical issues in terms of Cancer Diagnosis, Brain Tumor, Diabetic Retinopathy, Heart disease as well as drug design and development. The book will act as one-stop reference for readers to think and explore Swarm Intelligence and Evolutionary Algorithms seriously for real-time patient diagnosis, as the book provides solutions to various complex diseases found critical for medical practitioners to diagnose in real-world. Key Features: Highlights the importance and applications of Swarm Intelligence and Evolutionary Algorithms in Healthcare industry. Elaborates Swarm Intelligence and Evolutionary Algorithms for Cancer Detection. In-depth coverage of computational methodologies, approaches and techniques based on Swarm Intelligence and Evolutionary Algorithms for detecting Brain Tumour including deep learning to optimize brain tumor diagnosis. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms. Focuses on applying Swarm Intelligence and Evolutionary Algorithms for Heart Disease detection and diagnosis. Comprehensively covers the role of Swarm Intelligence and Evolutionary Algorithms for Drug Design and Discovery. The book will play a significant role for Researchers, Medical Practitioners, Healthcare Professionals and Industrial Healthcare Research and Development wings to conduct advanced research in Healthcare using Swarm Intelligence and Evolutionary Algorithms techniques.
Optimization of Pharmaceutical Processes by Antonios Fytopoulos,Rohit Ramachandran,Panos M. Pardalos Pdf
Optimization of Pharmaceutical Processes presents contributions from leading authorities in the fields of optimization and pharmaceutical manufacturing. Formulated within structured frameworks, practical examples and applications are given as guidance to apply optimization techniques to most aspects of pharmaceutical processes from design, to lab and pilot scale, and finally to manufacturing. The increasing demand for better quality, higher yield, more efficient-optimized and green pharmaceutical processes, indicates that optimal conditions for production must be applied to achieve simplicity, lower costs and superior yield. The application of such methods in the pharmaceutical industry is not trivial. Quality of the final product is of major importance to human health and the need for deep knowledge of the process parameters and the optimization of the processes are imperative. The volume, which includes new methods as well as review contributions will benefit a wide readership including engineers in pharmaceuticals, chemical, biological, to name just a few.
